NYTimes:Blogs:: Popular classification:: interactions
bdanalytics
Date: (Wed) Jun 03, 2015
Introduction:
Data: Source: Training: https://kaggle2.blob.core.windows.net/competitions-data/kaggle/4347/NYTimesBlogTrain.csv New: https://kaggle2.blob.core.windows.net/competitions-data/kaggle/4347/NYTimesBlogTest.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_) -> Done in this version
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(4) # max(length(glb_txt_vars), glb_n_cv_folds) + 1
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://kaggle2.blob.core.windows.net/competitions-data/kaggle/4347/NYTimesBlogTrain.csv"
glb_newdt_url <- "https://kaggle2.blob.core.windows.net/competitions-data/kaggle/4347/NYTimesBlogTest.csv"
glb_out_pfx <- "NYTBlogs_interactions_"
glb_save_envir <- FALSE #TRUE or
glb_is_separate_newent_dataset <- TRUE # or TRUE
glb_split_entity_newent_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- "<col_name> <condition_operator> <value>" # or NULL
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_drop_vars <- c(NULL) # or c("<col_name>")
#glb_max_fitent_obs <- 2238 # NULL # or any integer
glb_max_fitent_obs <- NULL # or any integer
glb_is_regression <- FALSE; glb_is_classification <- TRUE; glb_is_binomial <- TRUE
glb_rsp_var_raw <- "Popular"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Popular.fctr"
# if the response factor is based on numbers e.g (0/1 vs. "A"/"B"),
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) {
relevel(factor(ifelse(raw == 1, "Y", "N")), as.factor(c("Y", "N")), ref="N")
#as.factor(paste0("B", raw))
#as.factor(raw)
}
glb_map_rsp_raw_to_var(c(1, 1, 0, 0, 0))## [1] Y Y N N N
## Levels: N Yglb_map_rsp_var_to_raw <- function(var) {
as.numeric(var) - 1
#as.numeric(var)
#levels(var)[as.numeric(var)]
#c(" <=50K", " >50K")[as.numeric(var)]
}
glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 1, 0, 0, 0)))## [1] 1 1 0 0 0if ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# NewsDesk = the New York Times desk that produced the story
# SectionName = the section the article appeared in (Opinion, Arts, Technology, etc.)
# SubsectionName = the subsection the article appeared in (Education, Small Business, Room for Debate, etc.)
# Headline = the title of the article
# Snippet = a small portion of the article text
# Abstract = a summary of the blog article, written by the New York Times
# WordCount = the number of words in the article
# created WordCount.log
# PubDate = the publication date, in the format "Year-Month-Day Hour:Minute:Second"
glb_date_vars <- c("PubDate")
# UniqueID = a unique identifier for each article
glb_id_vars <- c("UniqueID")
glb_is_textual <- TRUE # vs. glb_is_numerical ???
#Sys.setlocale("LC_ALL", "C") # For english
glb_txt_vars <- c("Headline", "Snippet", "Abstract")
glb_append_stop_words <- list() # NULL # or c("<freq_word>")
# Remember to use unstemmed words
#orderBy(~ -cor.y.abs, subset(glb_feats_df, grepl("[HSA]\\.T\\.", id) & !is.na(cor.high.X)))
#dsp_obs(Headline.contains="polit")
#subset(glb_allobs_df, H.T.compani > 0)[, c("UniqueID", "Headline", "H.T.compani")]
glb_append_stop_words[["Headline"]] <- c(NULL
# Highly correlated to other features
# , "billion", "clip", "politics", "read", "springsummer"
)
#subset(glb_allobs_df, S.T.newyorktim > 0)[, c("UniqueID", "Snippet", "S.T.newyorktim")]
#glb_txt_lst[["Snippet"]][which(glb_allobs_df$UniqueID %in% c(8394, 8317, 8339, 8350, 8307))]
glb_append_stop_words[["Snippet"]] <- c(NULL
# Highly correlated to other features
# , "can"
# , "company", "companies", "companys"
# , "make"
# , "new", "newyorktimes"
# , "obama", "report", "said", "take", "time"
)
glb_append_stop_words[["Abstract"]] <- c(NULL
# ,"archives", "articles", "diary", "first", "herald", "president", "share", "show", "tribune" # These are repeated in Snippet terms
# ,"highlight", "highlighted", "highlighting", "highlights" # Correlated with S.T.highlight
# ,"make", "makes" # correlated with S.T.make
# ,"week", "weekly", "weeks" # correlated with S.T.week
)
# Remember to use stemmed terms
glb_important_terms <- list()
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
glb_sprs_thresholds <- c(0.988, 0.970, 0.970) # Generates 29, 22, 22 terms
#glb_sprs_thresholds <- c(0.990, 0.970, 0.970) # Generates 41, 22, 22 terms
#glb_sprs_thresholds <- c(0.985, 0.970, 0.970) # Generates 16, 22, 22 terms
#glb_sprs_thresholds <- c(0.975, 0.965, 0.965) # Generates 08, 14, 14 terms
#glb_sprs_thresholds <- c(0.982, 0.980, 0.980) # Generates 10, 61, 62 terms
names(glb_sprs_thresholds) <- glb_txt_vars
# List transformed vars
glb_exclude_vars_as_features <- c(NULL) # or c("<var_name>")
if (glb_is_textual)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
# List output vars (useful during testing in console)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# grep(glb_rsp_var_out, names(glb_trnobs_df), value=TRUE))
glb_interaction_only_features <- NULL
glb_impute_na_data <- TRUE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- TRUE# or FALSE
glb_models_lst <- list(); glb_models_df <- data.frame()
# rpart: .rnorm messes with the models badly
# caret creates dummy vars for factor feats which messes up the tuning
# - better to feed as.numeric(<feat>.fctr) to caret
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "rpart", "rf") else
# Classification
if (glb_is_binomial)
#glb_models_method_vctr <- c("glm", "rpart", "rf") else
#glb_models_method_vctr <- c("glm", "bayesglm", "rpart") else
glb_models_method_vctr <- c("glm", "rpart") else
glb_models_method_vctr <- c("rpart", "rf")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=080, max=100, by=10),
#data.frame(parameter="mtry", min=08, max=10, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.Accuracy.OOB", "max.auc.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL # or "<model_id_prefix>.<model_method>"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 8.548 NA NAStep 1.0: import data
chunk option: eval=
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/NYTimesBlogTrain.csv..."
## [1] "dimensions of data in ./data/NYTimesBlogTrain.csv: 6,532 rows x 10 cols"
## NewsDesk SectionName SubsectionName
## 1 Business Crosswords/Games
## 2 Culture Arts
## 3 Business Business Day Dealbook
## 4 Business Business Day Dealbook
## 5 Science Health
## 6 Science Health
## Headline
## 1 More School Daze
## 2 New 96-Page Murakami Work Coming in December
## 3 Public Pension Funds Stay Mum on Corporate Expats
## 4 Boot Camp for Bankers
## 5 Of Little Help to Older Knees
## 6 A Benefit of Legal Marijuana
## Snippet
## 1 A puzzle from Ethan Cooper that reminds me that a bill is due.
## 2 The Strange Library will arrive just three and a half months after Mr. Murakamis latest novel, Colorless Tsukuru Tazaki and His Years of Pilgrimage.
## 3 Public pension funds have major stakes in American companies moving overseas to cut their tax bills. But they are saying little about the strategy, which could hurt the nations tax base.
## 4 As they struggle to find new business to bolster sluggish earnings, banks consider the nations 25 million veterans and service members ideal customers.
## 5 Middle-aged and older patients are unlikely to benefit in the long term from surgery to repair tears in the meniscus, pads of cartilage in the knee, a new review of studies has found.
## 6 A new study has found evidence that legal access to marijuana is associated with fewer opioid overdose deaths, but researchers said their findings should not be used as the basis for the wide adoption of legalized cannabis.
## Abstract
## 1 A puzzle from Ethan Cooper that reminds me that a bill is due.
## 2 The Strange Library will arrive just three and a half months after Mr. Murakamis latest novel, Colorless Tsukuru Tazaki and His Years of Pilgrimage.
## 3 Public pension funds have major stakes in American companies moving overseas to cut their tax bills. But they are saying little about the strategy, which could hurt the nations tax base.
## 4 As they struggle to find new business to bolster sluggish earnings, banks consider the nations 25 million veterans and service members ideal customers.
## 5 Middle-aged and older patients are unlikely to benefit in the long term from surgery to repair tears in the meniscus, pads of cartilage in the knee, a new review of studies has found.
## 6 A new study has found evidence that legal access to marijuana is associated with fewer opioid overdose deaths, but researchers said their findings should not be used as the basis for the wide adoption of legalized cannabis.
## WordCount PubDate Popular UniqueID
## 1 508 2014-09-01 22:00:09 1 1
## 2 285 2014-09-01 21:14:07 0 2
## 3 1211 2014-09-01 21:05:36 0 3
## 4 1405 2014-09-01 20:43:34 1 4
## 5 181 2014-09-01 18:58:51 1 5
## 6 245 2014-09-01 18:52:22 1 6
## NewsDesk SectionName SubsectionName
## 226 Styles
## 995
## 3327
## 4753 Multimedia
## 4802 Business Crosswords/Games
## 6463 TStyle
## Headline
## 226 For Tavi Gevinson, Fashion Takes a Back Seat, for Now
## 995 Reconsidering What to Call an Extremist Group
## 3327 Clinton's Diagnosis of What's Wrong With Politics
## 4753 'Off Color' and on Target About Race in America
## 4802 Daniel Finkel's Circle-Toss Game
## 6463 Entering the Void
## Snippet
## 226 Tavi Gevinson, the teenage fashion star turned Broadway actress, wont be much of a player at New York Fashion Week this season.
## 995 Editors have decided to adjust how The Times refer to an Islamic extremist group that controls territory in Syria and Iraq.
## 3327 Hillary Rodham Clinton continued to laugh off questions about her presidential aspirations on Tuesday, but she did shed some light on what she thinks is wrong in Washington.
## 4753 Off Color, a New York Times video series, looks at how artists of color are making sharp social commentary about race in America through comedy and performance.
## 4802 By math educator Daniel Finkel, a puzzle thats childs play. Can you figure it out?
## 6463 The Spanish artist Miquel Barcel closely examines the basic materials of life in response to Edward Hirsch questioning his own belief in a higher power.
## Abstract
## 226 Tavi Gevinson, the teenage fashion star turned Broadway actress, wont be much of a player at New York Fashion Week this season.
## 995 Editors have decided to adjust how The Times refer to an Islamic extremist group that controls territory in Syria and Iraq.
## 3327 Hillary Rodham Clinton continued to laugh off questions about her presidential aspirations on Tuesday, but she did shed some light on what she thinks is wrong in Washington.
## 4753 Off Color, a New York Times video series, looks at how artists of color are making sharp social commentary about race in America through comedy and performance.
## 4802 By math educator Daniel Finkel, a puzzle thats childs play. Can you figure it out?
## 6463 The Spanish artist Miquel Barcel closely examines the basic materials of life in response to Edward Hirsch questioning his own belief in a higher power.
## WordCount PubDate Popular UniqueID
## 226 459 2014-09-04 16:55:57 0 226
## 995 301 2014-09-15 16:05:13 0 995
## 3327 236 2014-10-14 14:45:51 0 3327
## 4753 393 2014-11-02 05:00:13 0 4753
## 4802 1628 2014-11-03 12:00:04 1 4802
## 6463 264 2014-11-27 12:00:09 0 6463
## NewsDesk SectionName SubsectionName
## 6527 Foreign
## 6528 Opinion Room For Debate
## 6529 Foreign
## 6530 TStyle
## 6531 Multimedia
## 6532 Business
## Headline
## 6527 1914: Russians Dominate in East Poland
## 6528 Finding a Secretary of Defense
## 6529 1889: Metropolitan Opera House Reopens in New York
## 6530 The Daily Gift: Picasso Plates for Creative Dining
## 6531 Racing From New York to Barcelona
## 6532 Math Anxiety: Why Hollywood Makes Robots of Alan Turing and Other Geniuses
## Snippet
## 6527 From the International Herald Tribune archives: Russians dominate in East Poland in 1914.
## 6528 If Chuck Hagel isn't the right Pentagon chief to respond to an onslaught of global crises, who is?
## 6529 From the International Herald Tribune archives: The Metropolitan Opera House reopens in New York in 1889.
## 6530 Each day until Christmas, the editors of T share a new holiday gift idea.
## 6531 A sailboat race from New York to Barcelona was the setting for a thrilling and sometimes terrifying video about this challenging sport.
## 6532 The visionary who stares at formulas written on walls or mirrors or better yet, thin air has become a Hollywood trope. So has the depiction of the genius who cant connect with real people.
## Abstract
## 6527 From the International Herald Tribune archives: Russians dominate in East Poland in 1914.
## 6528 If Chuck Hagel isn't the right Pentagon chief to respond to an onslaught of global crises, who is?
## 6529 From the International Herald Tribune archives: The Metropolitan Opera House reopens in New York in 1889.
## 6530 Each day until Christmas, the editors of T share a new holiday gift idea.
## 6531 A sailboat race from New York to Barcelona was the setting for a thrilling and sometimes terrifying video about this challenging sport.
## 6532 The visionary who stares at formulas written on walls or mirrors or better yet, thin air has become a Hollywood trope. So has the depiction of the genius who cant connect with real people.
## WordCount PubDate Popular UniqueID
## 6527 176 2014-11-30 13:48:40 0 6527
## 6528 1597 2014-11-30 13:27:23 0 6528
## 6529 214 2014-11-30 09:44:57 0 6529
## 6530 61 2014-11-30 09:00:43 0 6530
## 6531 441 2014-11-30 09:00:22 0 6531
## 6532 921 2014-11-30 07:00:40 0 6532
## 'data.frame': 6532 obs. of 10 variables:
## $ NewsDesk : chr "Business" "Culture" "Business" "Business" ...
## $ SectionName : chr "Crosswords/Games" "Arts" "Business Day" "Business Day" ...
## $ SubsectionName: chr "" "" "Dealbook" "Dealbook" ...
## $ Headline : chr "More School Daze" "New 96-Page Murakami Work Coming in December" "Public Pension Funds Stay Mum on Corporate Expats" "Boot Camp for Bankers" ...
## $ Snippet : chr "A puzzle from Ethan Cooper that reminds me that a bill is due." "The Strange Library will arrive just three and a half months after Mr. Murakamis latest novel, Colorless Tsukuru Tazaki and His"| __truncated__ "Public pension funds have major stakes in American companies moving overseas to cut their tax bills. But they are saying little"| __truncated__ "As they struggle to find new business to bolster sluggish earnings, banks consider the nations 25 million veterans and service "| __truncated__ ...
## $ Abstract : chr "A puzzle from Ethan Cooper that reminds me that a bill is due." "The Strange Library will arrive just three and a half months after Mr. Murakamis latest novel, Colorless Tsukuru Tazaki and His"| __truncated__ "Public pension funds have major stakes in American companies moving overseas to cut their tax bills. But they are saying little"| __truncated__ "As they struggle to find new business to bolster sluggish earnings, banks consider the nations 25 million veterans and service "| __truncated__ ...
## $ WordCount : int 508 285 1211 1405 181 245 258 893 1077 188 ...
## $ PubDate : chr "2014-09-01 22:00:09" "2014-09-01 21:14:07" "2014-09-01 21:05:36" "2014-09-01 20:43:34" ...
## $ Popular : int 1 0 0 1 1 1 0 1 1 0 ...
## $ UniqueID : int 1 2 3 4 5 6 7 8 9 10 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULLif (glb_is_separate_newent_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newent_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newent_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## [1] "Reading file ./data/NYTimesBlogTest.csv..."
## [1] "dimensions of data in ./data/NYTimesBlogTest.csv: 1,870 rows x 9 cols"
## NewsDesk SectionName SubsectionName
## 1 Culture
## 2 Culture Arts
## 3 Business Crosswords/Games
## 4 Business Business Day Dealbook
## 5 Science Health
## 6 Science Health
## Headline
## 1 'Birdman' Tops the Gothams
## 2 'Sleepy Hollow' Recap: A Not-So-Shocking Death
## 3 Drinking Buddy For Falstaff
## 4 Encouraging Public Service, Through Wall Street's 'Revolving Door'
## 5 Therapy Prevents Repeat Suicide Attempts
## 6 Hoping for a Good Death
## Snippet
## 1 The backstage tale won two awards; Citizenfour, the Edward Snowden documentary, was also a winner.
## 2 In the fall season finale, a question of where the series has many places to go.
## 3 In which Timothy Polin reveals his potty mouth.
## 4 The debate about pay for Wall Street executives who take government jobs appears to be based more on a populist shakedown than on good public policy.
## 5 Short-term psychotherapy may be an effective way to prevent repeated suicide attempts.
## 6 What I hadnt considered before my fathers heart attack was the precise meaning of not wanting to live hooked up to machines.
## Abstract
## 1 The backstage tale won two awards; Citizenfour, the Edward Snowden documentary, was also a winner.
## 2 In the fall season finale, a question of where the series has many places to go.
## 3 In which Timothy Polin reveals his potty mouth.
## 4 The debate about pay for Wall Street executives who take government jobs appears to be based more on a populist shakedown than on good public policy.
## 5 Short-term psychotherapy may be an effective way to prevent repeated suicide attempts.
## 6 What I hadnt considered before my fathers heart attack was the precise meaning of not wanting to live hooked up to machines.
## WordCount PubDate UniqueID
## 1 111 2014-12-01 22:45:24 6533
## 2 558 2014-12-01 22:01:34 6534
## 3 788 2014-12-01 22:00:26 6535
## 4 915 2014-12-01 21:04:13 6536
## 5 213 2014-12-01 19:13:20 6537
## 6 938 2014-12-01 19:05:12 6538
## NewsDesk SectionName SubsectionName
## 3 Business Crosswords/Games
## 334 OpEd Opinion
## 725 TStyle
## 732 Business Business Day Dealbook
## 752 Business Business Day Dealbook
## 864
## Headline
## 3 Drinking Buddy For Falstaff
## 334 Facts & Figures: America’s Unique Take on Maternity Leave
## 725 Ansel Elgort Buttons Up in Brioni
## 732 A Shake-Up as the Financial World Infiltrates Philanthropy
## 752 Coupang, a South Korean E-Commerce Site, Raises $300 Million
## 864 Today in Politics
## Snippet
## 3 In which Timothy Polin reveals his potty mouth.
## 334 In the U.S., paid parental leave is more of a perk than a guarantee.
## 725 The actor brought a tinge of youthfulness to the classic Italian houses retro-tailored look.
## 732 Donor-advised funds help investors get deductions for charitable donations in one year, but society doesnt get the benefit of the money right away.
## 752 The latest financing round underscores Coupangs maturity and its ambitions to one day be a publicly traded company.
## 864 The 113th Congress is concluding with partisan brinksmanship and one last mad scramble for votes to pass a $1.1 trillion spending package.
## Abstract
## 3 In which Timothy Polin reveals his potty mouth.
## 334 In the U.S., paid parental leave is more of a perk than a guarantee.
## 725 The actor brought a tinge of youthfulness to the classic Italian houses retro-tailored look.
## 732 Donor-advised funds help investors get deductions for charitable donations in one year, but society doesnt get the benefit of the money right away.
## 752 The latest financing round underscores Coupangs maturity and its ambitions to one day be a publicly traded company.
## 864 The 113th Congress is concluding with partisan brinksmanship and one last mad scramble for votes to pass a $1.1 trillion spending package.
## WordCount PubDate UniqueID
## 3 788 2014-12-01 22:00:26 6535
## 334 160 2014-12-04 11:45:20 6866
## 725 89 2014-12-10 12:30:47 7257
## 732 1172 2014-12-10 12:00:38 7264
## 752 353 2014-12-10 08:30:41 7284
## 864 1544 2014-12-11 07:09:25 7396
## NewsDesk SectionName SubsectionName
## 1865
## 1866 Business Technology
## 1867 Metro N.Y. / Region
## 1868 Multimedia
## 1869 Foreign World Asia Pacific
## 1870 Science Health
## Headline
## 1865 Today in Politics
## 1866 Uber Suspends Operations in Spain
## 1867 New York Today: The Year in News
## 1868 New Year, Old Memories, in Times Square
## 1869 Hong Kong Police Criticized After 14-Year-Old's Detention
## 1870 The Super-Short Workout and Other Fitness Trends
## Snippet
## 1865 House Republicans are ending the year on a defensive note over Representative Steve Scalises 2002 speech to a white supremacist group.
## 1866 In a first in the growing pushback against Ubers global expansion, a judges ruling barred telecommunications operators and banks from supporting the companys services.
## 1867 Wednesday: The most read stories of 2014, teeth-chattering cold, and its New Years Eve.
## 1868 What happens when you combine Burning Man, Independence Day fireworks, the last day of school and a full-contact Black Friday sale-a-bration? New Years Eve in Times Square.
## 1869 The authorities have been accused of trying to intimidate young pro-democracy protesters and their families after a 14-year-old girl was detained on suspicion of drawing flowers in chalk near government headquarters and sent to a juvenile home.
## 1870 The big story in exercise science this year was the super-short workout, although many other fitness-related themes emerged in 2014.
## Abstract
## 1865 House Republicans are ending the year on a defensive note over Representative Steve Scalises 2002 speech to a white supremacist group.
## 1866 In a first in the growing pushback against Ubers global expansion, a judges ruling barred telecommunications operators and banks from supporting the companys services.
## 1867 Wednesday: The most read stories of 2014, teeth-chattering cold, and its New Years Eve.
## 1868 What happens when you combine Burning Man, Independence Day fireworks, the last day of school and a full-contact Black Friday sale-a-bration? New Years Eve in Times Square.
## 1869 The authorities have been accused of trying to intimidate young pro-democracy protesters and their families after a 14-year-old girl was detained on suspicion of drawing flowers in chalk near government headquarters and sent to a juvenile home.
## 1870 The big story in exercise science this year was the super-short workout, although many other fitness-related themes emerged in 2014.
## WordCount PubDate UniqueID
## 1865 1616 2014-12-31 07:03:46 8397
## 1866 292 2014-12-31 06:09:32 8398
## 1867 1010 2014-12-31 06:06:58 8399
## 1868 387 2014-12-31 05:00:19 8400
## 1869 717 2014-12-31 04:16:29 8401
## 1870 818 2014-12-31 00:01:10 8402
## 'data.frame': 1870 obs. of 9 variables:
## $ NewsDesk : chr "Culture" "Culture" "Business" "Business" ...
## $ SectionName : chr "" "Arts" "Crosswords/Games" "Business Day" ...
## $ SubsectionName: chr "" "" "" "Dealbook" ...
## $ Headline : chr "'Birdman' Tops the Gothams" "'Sleepy Hollow' Recap: A Not-So-Shocking Death" "Drinking Buddy For Falstaff" "Encouraging Public Service, Through Wall Street's 'Revolving Door'" ...
## $ Snippet : chr "The backstage tale won two awards; Citizenfour, the Edward Snowden documentary, was also a winner." "In the fall season finale, a question of where the series has many places to go." "In which Timothy Polin reveals his potty mouth." "The debate about pay for Wall Street executives who take government jobs appears to be based more on a populist shakedown than "| __truncated__ ...
## $ Abstract : chr "The backstage tale won two awards; Citizenfour, the Edward Snowden documentary, was also a winner." "In the fall season finale, a question of where the series has many places to go." "In which Timothy Polin reveals his potty mouth." "The debate about pay for Wall Street executives who take government jobs appears to be based more on a populist shakedown than "| __truncated__ ...
## $ WordCount : int 111 558 788 915 213 938 1336 2644 752 99 ...
## $ PubDate : chr "2014-12-01 22:45:24" "2014-12-01 22:01:34" "2014-12-01 22:00:26" "2014-12-01 21:04:13" ...
## $ UniqueID : int 6533 6534 6535 6536 6537 6538 6539 6540 6541 6542 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## NULLif (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
# Check for duplicates in glb_id_vars
if (length(glb_id_vars) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_id_vars <- ".rownames"
}
if (sum(duplicated(glb_allobs_df[, glb_id_vars, FALSE])) > 0)
stop(glb_id_vars, " duplicated in glb_allobs_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_vars)
# Combine trnent & newent into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
glb_trnobs_df <- glb_newobs_df <- NULL
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 8.548 9.496 0.949
## 2 inspect.data 2 0 9.497 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
dsp_problem_data <- function(df) {
print(sprintf("numeric data missing in %s: ",
ifelse(!is.null(df_name <- comment(df)), df_name, "")))
print(sapply(setdiff(names(df), myfind_chr_cols_df(df)),
function(col) sum(is.na(df[, col]))))
print(sprintf("numeric data w/ 0s in %s: ",
ifelse(!is.null(df_name <- comment(df)), df_name, "")))
print(sapply(setdiff(names(df), myfind_chr_cols_df(df)),
function(col) sum(df[, col] == 0, na.rm=TRUE)))
print(sprintf("numeric data w/ Infs in %s: ",
ifelse(!is.null(df_name <- comment(df)), df_name, "")))
print(sapply(setdiff(names(df), myfind_chr_cols_df(df)),
function(col) sum(df[, col] == Inf, na.rm=TRUE)))
print(sprintf("numeric data w/ NaNs in %s: ",
ifelse(!is.null(df_name <- comment(df)), df_name, "")))
print(sapply(setdiff(names(df), myfind_chr_cols_df(df)),
function(col) sum(df[, col] == NaN, na.rm=TRUE)))
print(sprintf("string data missing in %s: ",
ifelse(!is.null(df_name <- comment(df)), df_name, "")))
print(sapply(setdiff(myfind_chr_cols_df(df), ".src"),
function(col) sum(df[, col] == "")))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
dsp_problem_data(glb_allobs_df)
}
glb_chk_data()## Loading required package: reshape2
## Popular.0 Popular.1 Popular.NA
## Test NA NA 1870
## Train 5439 1093 NA
## Popular.0 Popular.1 Popular.NA
## Test NA NA 1
## Train 0.8326699 0.1673301 NA
## [1] "numeric data missing in glb_allobs_df: "
## WordCount Popular UniqueID
## 0 1870 0
## [1] "numeric data w/ 0s in glb_allobs_df: "
## WordCount Popular UniqueID
## 109 5439 0
## [1] "numeric data w/ Infs in glb_allobs_df: "
## WordCount Popular UniqueID
## 0 0 0
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## WordCount Popular UniqueID
## 0 0 0
## [1] "string data missing in glb_allobs_df: "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2408 2899 6176 0 13
## Abstract PubDate
## 17 0# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## Popular Popular.fctr .n
## 1 0 N 5439
## 2 NA <NA> 1870
## 3 1 Y 1093## Warning in loop_apply(n, do.ply): Removed 1 rows containing missing values
## (position_stack).
## Popular.fctr.N Popular.fctr.Y Popular.fctr.NA
## Test NA NA 1870
## Train 5439 1093 NA
## Popular.fctr.N Popular.fctr.Y Popular.fctr.NA
## Test NA NA 1
## Train 0.8326699 0.1673301 NA# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
myextract_dates_df <- function(df, vars, rsp_var) {
keep_feats <- c(NULL)
for (var in vars) {
dates_df <- data.frame(.date=strptime(df[, var], "%Y-%m-%d %H:%M:%S"))
dates_df[, rsp_var] <- df[, rsp_var]
dates_df[, paste0(var, ".POSIX")] <- dates_df$.date
dates_df[, paste0(var, ".year")] <- as.numeric(format(dates_df$.date, "%Y"))
dates_df[, paste0(var, ".year.fctr")] <- as.factor(format(dates_df$.date, "%Y"))
dates_df[, paste0(var, ".month")] <- as.numeric(format(dates_df$.date, "%m"))
dates_df[, paste0(var, ".month.fctr")] <- as.factor(format(dates_df$.date, "%m"))
dates_df[, paste0(var, ".date")] <- as.numeric(format(dates_df$.date, "%d"))
dates_df[, paste0(var, ".date.fctr")] <-
cut(as.numeric(format(dates_df$.date, "%d")), 5) # by month week
dates_df[, paste0(var, ".juliandate")] <- as.numeric(format(dates_df$.date, "%j"))
# wkday Sun=0; Mon=1; ...; Sat=6
dates_df[, paste0(var, ".wkday")] <- as.numeric(format(dates_df$.date, "%w"))
dates_df[, paste0(var, ".wkday.fctr")] <- as.factor(format(dates_df$.date, "%w"))
# Federal holidays 1.9., 13.10., 27.11., 25.12.
# NYState holidays 1.9., 13.10., 11.11., 27.11., 25.12.
months <- dates_df[, paste0(var, ".month")]
dates <- dates_df[, paste0(var, ".date")]
dates_df[, paste0(var, ".hlday")] <-
ifelse( ((months == 09) & (dates == 01)) |
((months == 10) & (dates == 13)) |
((months == 11) & (dates == 27)) |
((months == 12) & (dates == 25)) ,
1, 0)
dates_df[, paste0(var, ".wkend")] <- as.numeric(
(dates_df[, paste0(var, ".wkday")] %in% c(0, 6)) |
dates_df[, paste0(var, ".hlday")] )
dates_df[, paste0(var, ".hour")] <- as.numeric(format(dates_df$.date, "%H"))
dates_df[, paste0(var, ".hour.fctr")] <-
cut(as.numeric(format(dates_df$.date, "%H")), 3) # by work-shift
dates_df[, paste0(var, ".minute")] <- as.numeric(format(dates_df$.date, "%M"))
dates_df[, paste0(var, ".minute.fctr")] <-
cut(as.numeric(format(dates_df$.date, "%M")), 4) # by quarter-hours
dates_df[, paste0(var, ".second")] <- as.numeric(format(dates_df$.date, "%S"))
dates_df[, paste0(var, ".second.fctr")] <-
cut(as.numeric(format(dates_df$.date, "%S")), 4) # by quarter-hours
dates_df[, paste0(var, ".day.minutes")] <- 60 * dates_df[, paste0(var, ".hour")] +
dates_df[, paste0(var, ".minute")]
dates_df[, paste0(var, ".day.minutes.poly.", 1:5)] <-
as.matrix(poly(dates_df[, paste0(var, ".day.minutes")], 5))
# print(gp <- myplot_box(df=dates_df, ycol_names="PubDate.day.minutes",
# xcol_name=rsp_var))
# print(gp <- myplot_scatter(df=dates_df, xcol_name=".rownames",
# ycol_name="PubDate.day.minutes", colorcol_name=rsp_var))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name="PubDate.day.minutes.poly.1", colorcol_name=rsp_var))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.day.minutes",
# ycol_name="PubDate.day.minutes.poly.4", colorcol_name=rsp_var))
#
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name="PubDate.day.minutes", colorcol_name=rsp_var, smooth=TRUE))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name="PubDate.day.minutes.poly.4", colorcol_name=rsp_var, smooth=TRUE))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name=c("PubDate.day.minutes", "PubDate.day.minutes.poly.4"),
# colorcol_name=rsp_var))
print(gp <- myplot_scatter(df=subset(dates_df, Popular.fctr=="Y"),
xcol_name="PubDate.juliandate",
ycol_name="PubDate.day.minutes", colorcol_name=rsp_var))
print(gp <- myplot_box(df=dates_df, ycol_names="PubDate.second",
xcol_name=rsp_var))
print(gp <- myplot_bar(df=dates_df, ycol_names="PubDate.second.fctr",
xcol_name=rsp_var, colorcol_name="PubDate.second.fctr"))
keep_feats <- union(keep_feats, paste(var,
c(".POSIX", ".year.fctr", ".month.fctr", ".date.fctr", ".wkday.fctr",
".wkend", ".hour.fctr", ".minute.fctr", ".second.fctr",
paste0(".day.minutes.poly.", 1:5)), sep=""))
}
#myprint_df(dates_df)
return(dates_df[, keep_feats])
}
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars, rsp_var=glb_rsp_var))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
paste(glb_date_vars, c("", ".POSIX"), sep=""))
}
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
srt_entity_df <- orderBy(~PubDate.POSIX, glb_allobs_df)
print(myplot_scatter(subset(srt_entity_df,
PubDate.POSIX < strptime("2014-09-02", "%Y-%m-%d")),
xcol_name="PubDate.POSIX", ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var
))
# Create features that measure the gap between previous timestamp in the data
require(zoo)## Loading required package: zoo
##
## Attaching package: 'zoo'
##
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericpd = as.POSIXlt(srt_entity_df$PubDate)
z = zoo(as.numeric(pd))
srt_entity_df[, "PubDate.zoo"] <- z
print(head(srt_entity_df))## NewsDesk SectionName SubsectionName
## 33 Science Health
## 32 Foreign World Asia Pacific
## 31 Multimedia
## 30 Culture Arts
## 29 Business Business Day Dealbook
## 28 Magazine Magazine
## Headline
## 33 Don't Catch What Ails Your House
## 32 Ukraine Conflict Has Been a Lift for China, Scholars Say
## 31 Revisiting Life and Death in Africa
## 30 Fabio Luisi Has a New Gig
## 29 Heineken to Sell Mexican Packaging Unit to Crown Holdings
## 28 Behind the Cover Story: Emily Bazelon on Medical Abortion Through the Mail
## Snippet
## 33 It doesnt take a flood to encourage the growth of mold in a home. A moist environment will do. A runny nose, coughing and all the rest typically follow.
## 32 As the United States and the European Union have imposed sanctions on Russia over the unrest in eastern Ukraine, China has been able to stand apart and gain concrete advantages, experts on foreign policy say.
## 31 Yunghi Kim went to Somalia 20 years ago expecting to cover a famine. She found herself instead in a war zone.
## 30 The music director of the Zurich Opera and principal conductor of the Metropolitan Opera will be named principal conductor of the Danish National Symphony Orchestra.
## 29 The deal values the container unit Empaque at about $1.2 billion and would make Crown Holdings the second-largest beverage can producer in North America.
## 28 Emily Bazelon, a contributing writer for the magazine, wrote this weeks cover story about the online distribution of medical abortions. Here she discusses reporting on a group of activists working to provide medical abortions through the mail.
## Abstract
## 33 It doesnt take a flood to encourage the growth of mold in a home. A moist environment will do. A runny nose, coughing and all the rest typically follow.
## 32 As the United States and the European Union have imposed sanctions on Russia over the unrest in eastern Ukraine, China has been able to stand apart and gain concrete advantages, experts on foreign policy say.
## 31 Yunghi Kim went to Somalia 20 years ago expecting to cover a famine. She found herself instead in a war zone.
## 30 The music director of the Zurich Opera and principal conductor of the Metropolitan Opera will be named principal conductor of the Danish National Symphony Orchestra.
## 29 The deal values the container unit Empaque at about $1.2 billion and would make Crown Holdings the second-largest beverage can producer in North America.
## 28 Emily Bazelon, a contributing writer for the magazine, wrote this weeks cover story about the online distribution of medical abortions. Here she discusses reporting on a group of activists working to provide medical abortions through the mail.
## WordCount PubDate Popular UniqueID .src Popular.fctr
## 33 962 2014-09-01 00:01:32 1 33 Train Y
## 32 529 2014-09-01 02:48:41 0 32 Train N
## 31 832 2014-09-01 03:00:15 0 31 Train N
## 30 166 2014-09-01 04:00:06 0 30 Train N
## 29 442 2014-09-01 04:11:20 0 29 Train N
## 28 1190 2014-09-01 05:00:26 0 28 Train N
## PubDate.POSIX PubDate.year.fctr PubDate.month.fctr
## 33 2014-09-01 00:01:32 2014 09
## 32 2014-09-01 02:48:41 2014 09
## 31 2014-09-01 03:00:15 2014 09
## 30 2014-09-01 04:00:06 2014 09
## 29 2014-09-01 04:11:20 2014 09
## 28 2014-09-01 05:00:26 2014 09
## PubDate.date.fctr PubDate.wkday.fctr PubDate.wkend PubDate.hour.fctr
## 33 (0.97,7] 1 1 (-0.023,7.67]
## 32 (0.97,7] 1 1 (-0.023,7.67]
## 31 (0.97,7] 1 1 (-0.023,7.67]
## 30 (0.97,7] 1 1 (-0.023,7.67]
## 29 (0.97,7] 1 1 (-0.023,7.67]
## 28 (0.97,7] 1 1 (-0.023,7.67]
## PubDate.minute.fctr PubDate.second.fctr PubDate.day.minutes.poly.1
## 33 (-0.059,14.8] (29.5,44.2] -0.02745833
## 32 (44.2,59.1] (29.5,44.2] -0.02139413
## 31 (-0.059,14.8] (14.8,29.5] -0.02095838
## 30 (-0.059,14.8] (-0.059,14.8] -0.01877962
## 29 (-0.059,14.8] (14.8,29.5] -0.01838018
## 28 (-0.059,14.8] (14.8,29.5] -0.01660087
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.3
## 33 0.04254377 -0.044820236
## 32 0.02175312 -0.007057785
## 31 0.02046604 -0.005257251
## 30 0.01444664 0.002155510
## 29 0.01341829 0.003242535
## 28 0.00912060 0.007145007
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.5 PubDate.zoo
## 33 0.03365827 -0.02362780 1409544092
## 32 -0.01440172 0.03180561 1409554121
## 31 -0.01557161 0.03121768 1409554815
## 30 -0.01830080 0.02423360 1409558406
## 29 -0.01831901 0.02246133 1409559080
## 28 -0.01700098 0.01380338 1409562026print(myplot_scatter(subset(srt_entity_df,
PubDate.POSIX < strptime("2014-09-02", "%Y-%m-%d")),
xcol_name="PubDate.zoo", ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var
))## Don't know how to automatically pick scale for object of type zoo. Defaulting to continuous
n = nrow(srt_entity_df)
b = zoo(, seq(n))
last1 = as.numeric(merge(z-lag(z, -1), b, all = TRUE))
srt_entity_df[, "PubDate.last1"] <- last1
srt_entity_df[is.na(srt_entity_df$PubDate.last1), "PubDate.last1"] <- 0
srt_entity_df[, "PubDate.last1.log"] <- log(1 + srt_entity_df[, "PubDate.last1"])
print(gp <- myplot_box(df=subset(srt_entity_df, PubDate.last1.log > 0),
ycol_names="PubDate.last1.log",
xcol_name=glb_rsp_var))
last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
srt_entity_df[, "PubDate.last10"] <- last10
srt_entity_df[is.na(srt_entity_df$PubDate.last10), "PubDate.last10"] <- 0
srt_entity_df[, "PubDate.last10.log"] <- log(1 + srt_entity_df[, "PubDate.last10"])
print(gp <- myplot_box(df=subset(srt_entity_df, PubDate.last10.log > 0),
ycol_names="PubDate.last10.log",
xcol_name=glb_rsp_var))
last100 = as.numeric(merge(z-lag(z, -100), b, all = TRUE))
srt_entity_df[, "PubDate.last100"] <- last100
srt_entity_df[is.na(srt_entity_df$PubDate.last100), "PubDate.last100"] <- 0
srt_entity_df[, "PubDate.last100.log"] <- log(1 + srt_entity_df[, "PubDate.last100"])
print(gp <- myplot_box(df=subset(srt_entity_df, PubDate.last100.log > 0),
ycol_names="PubDate.last100.log",
xcol_name=glb_rsp_var))
glb_allobs_df <- srt_entity_df
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c("PubDate.zoo", "PubDate.last1", "PubDate.last10", "PubDate.last100"))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
# check distribution of all numeric data
dsp_numeric_vars_dstrb <- function(vars_lst) {
for (var in vars_lst) {
print(sprintf("var: %s", var))
gp <- myplot_box(df=glb_allobs_df, ycol_names=var, xcol_name=glb_rsp_var)
if (inherits(glb_allobs_df[, var], "factor"))
gp <- gp + facet_wrap(reformulate(var))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
# union(myfind_chr_cols_df(glb_allobs_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>.log=log(1 + <col.name>),
WordCount.log = log(1 + WordCount),
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
.rnorm=rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
# Add WordCount.log since WordCount is not distributed normally
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyrprint("Replacing WordCount with WordCount.log in potential feature set")## [1] "Replacing WordCount with WordCount.log in potential feature set"glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, "WordCount")
# Remove PubDate.year since all entity data is from 2014
# Remove PubDate.month.fctr since all newent data is from December
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c("PubDate.year", "PubDate.month.fctr"))
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
union(myfind_chr_cols_df(glb_allobs_df),
union(glb_rsp_var_raw,
union(glb_rsp_var, glb_exclude_vars_as_features)))))## [1] "var: PubDate.year.fctr"
## [1] "var: PubDate.date.fctr"
## [1] "var: PubDate.wkday.fctr"
## [1] "var: PubDate.wkend"
## [1] "var: PubDate.hour.fctr"
## [1] "var: PubDate.minute.fctr"
## [1] "var: PubDate.second.fctr"
## [1] "var: PubDate.day.minutes.poly.1"
## [1] "var: PubDate.day.minutes.poly.2"
## [1] "var: PubDate.day.minutes.poly.3"
## [1] "var: PubDate.day.minutes.poly.4"
## [1] "var: PubDate.day.minutes.poly.5"
## [1] "var: PubDate.last1.log"
## [1] "var: PubDate.last10.log"
## [1] "var: PubDate.last100.log"
## [1] "var: WordCount.log"
## [1] "var: .rnorm"
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("KO", "PG")),
# "Date.my", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.Date("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.Date("1983-01-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5))
rm(srt_entity_df, last1, last10, last100, pd)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cleanse.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 2 inspect.data 2 0 9.497 30.891 21.394
## 3 cleanse.data 2 1 30.891 NA NAStep 2.1: cleanse data
# Options:
# 1. Not fill missing vars
# 2. Fill missing numerics with a different algorithm
# 3. Fill missing chars with data based on clusters
dsp_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## WordCount Popular
## 0 1870
## UniqueID Popular.fctr
## 0 1870
## PubDate.POSIX PubDate.year.fctr
## 0 0
## PubDate.month.fctr PubDate.date.fctr
## 0 0
## PubDate.wkday.fctr PubDate.wkend
## 0 0
## PubDate.hour.fctr PubDate.minute.fctr
## 0 0
## PubDate.second.fctr PubDate.day.minutes.poly.1
## 0 0
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.3
## 0 0
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.5
## 0 0
## PubDate.zoo PubDate.last1
## 0 0
## PubDate.last1.log PubDate.last10
## 0 0
## PubDate.last10.log PubDate.last100
## 0 0
## PubDate.last100.log WordCount.log
## 0 0
## .rnorm
## 0
## [1] "numeric data w/ 0s in : "
## WordCount Popular
## 109 5439
## UniqueID Popular.fctr
## 0 0
## PubDate.POSIX PubDate.year.fctr
## 0 0
## PubDate.month.fctr PubDate.date.fctr
## 0 0
## PubDate.wkday.fctr PubDate.wkend
## 378 7624
## PubDate.hour.fctr PubDate.minute.fctr
## 0 0
## PubDate.second.fctr PubDate.day.minutes.poly.1
## 0 0
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.3
## 0 0
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.5
## 0 0
## PubDate.zoo PubDate.last1
## 0 11
## PubDate.last1.log PubDate.last10
## 11 10
## PubDate.last10.log PubDate.last100
## 10 100
## PubDate.last100.log WordCount.log
## 100 109
## .rnorm
## 0
## [1] "numeric data w/ Infs in : "
## WordCount Popular
## 0 0
## UniqueID Popular.fctr
## 0 0
## PubDate.POSIX PubDate.year.fctr
## 0 0
## PubDate.month.fctr PubDate.date.fctr
## 0 0
## PubDate.wkday.fctr PubDate.wkend
## 0 0
## PubDate.hour.fctr PubDate.minute.fctr
## 0 0
## PubDate.second.fctr PubDate.day.minutes.poly.1
## 0 0
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.3
## 0 0
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.5
## 0 0
## PubDate.zoo PubDate.last1
## 0 0
## PubDate.last1.log PubDate.last10
## 0 0
## PubDate.last10.log PubDate.last100
## 0 0
## PubDate.last100.log WordCount.log
## 0 0
## .rnorm
## 0
## [1] "numeric data w/ NaNs in : "
## WordCount Popular
## 0 0
## UniqueID Popular.fctr
## 0 0
## PubDate.POSIX PubDate.year.fctr
## 0 0
## PubDate.month.fctr PubDate.date.fctr
## 0 0
## PubDate.wkday.fctr PubDate.wkend
## 0 0
## PubDate.hour.fctr PubDate.minute.fctr
## 0 0
## PubDate.second.fctr PubDate.day.minutes.poly.1
## 0 0
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.3
## 0 0
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.5
## 0 0
## PubDate.zoo PubDate.last1
## 0 0
## PubDate.last1.log PubDate.last10
## 0 0
## PubDate.last10.log PubDate.last100
## 0 0
## PubDate.last100.log WordCount.log
## 0 0
## .rnorm
## 0
## [1] "string data missing in : "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2408 2899 6176 0 13
## Abstract PubDate
## 17 0warning("Forcing ", nrow(subset(glb_allobs_df, WordCount.log == 0)),
" obs with WordCount.log 0s to NA")## Warning: Forcing 109 obs with WordCount.log 0s to NAglb_allobs_df[glb_allobs_df$WordCount.log == 0, "WordCount.log"] <- NA
dsp_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## WordCount Popular
## 0 1870
## UniqueID Popular.fctr
## 0 1870
## PubDate.POSIX PubDate.year.fctr
## 0 0
## PubDate.month.fctr PubDate.date.fctr
## 0 0
## PubDate.wkday.fctr PubDate.wkend
## 0 0
## PubDate.hour.fctr PubDate.minute.fctr
## 0 0
## PubDate.second.fctr PubDate.day.minutes.poly.1
## 0 0
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.3
## 0 0
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.5
## 0 0
## PubDate.zoo PubDate.last1
## 0 0
## PubDate.last1.log PubDate.last10
## 0 0
## PubDate.last10.log PubDate.last100
## 0 0
## PubDate.last100.log WordCount.log
## 0 109
## .rnorm
## 0
## [1] "numeric data w/ 0s in : "
## WordCount Popular
## 109 5439
## UniqueID Popular.fctr
## 0 0
## PubDate.POSIX PubDate.year.fctr
## 0 0
## PubDate.month.fctr PubDate.date.fctr
## 0 0
## PubDate.wkday.fctr PubDate.wkend
## 378 7624
## PubDate.hour.fctr PubDate.minute.fctr
## 0 0
## PubDate.second.fctr PubDate.day.minutes.poly.1
## 0 0
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.3
## 0 0
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.5
## 0 0
## PubDate.zoo PubDate.last1
## 0 11
## PubDate.last1.log PubDate.last10
## 11 10
## PubDate.last10.log PubDate.last100
## 10 100
## PubDate.last100.log WordCount.log
## 100 0
## .rnorm
## 0
## [1] "numeric data w/ Infs in : "
## WordCount Popular
## 0 0
## UniqueID Popular.fctr
## 0 0
## PubDate.POSIX PubDate.year.fctr
## 0 0
## PubDate.month.fctr PubDate.date.fctr
## 0 0
## PubDate.wkday.fctr PubDate.wkend
## 0 0
## PubDate.hour.fctr PubDate.minute.fctr
## 0 0
## PubDate.second.fctr PubDate.day.minutes.poly.1
## 0 0
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.3
## 0 0
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.5
## 0 0
## PubDate.zoo PubDate.last1
## 0 0
## PubDate.last1.log PubDate.last10
## 0 0
## PubDate.last10.log PubDate.last100
## 0 0
## PubDate.last100.log WordCount.log
## 0 0
## .rnorm
## 0
## [1] "numeric data w/ NaNs in : "
## WordCount Popular
## 0 0
## UniqueID Popular.fctr
## 0 0
## PubDate.POSIX PubDate.year.fctr
## 0 0
## PubDate.month.fctr PubDate.date.fctr
## 0 0
## PubDate.wkday.fctr PubDate.wkend
## 0 0
## PubDate.hour.fctr PubDate.minute.fctr
## 0 0
## PubDate.second.fctr PubDate.day.minutes.poly.1
## 0 0
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.3
## 0 0
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.5
## 0 0
## PubDate.zoo PubDate.last1
## 0 0
## PubDate.last1.log PubDate.last10
## 0 0
## PubDate.last10.log PubDate.last100
## 0 0
## PubDate.last100.log WordCount.log
## 0 0
## .rnorm
## 0
## [1] "string data missing in : "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2408 2899 6176 0 13
## Abstract PubDate
## 17 0dsp_catgs <- function() {
print("NewsDesk:")
print(table(glb_allobs_df$NewsDesk))
print("SectionName:")
print(table(glb_allobs_df$SectionName))
print("SubsectionName:")
print(table(glb_allobs_df$SubsectionName))
}
sel_obs <- function(Popular=NULL,
NewsDesk=NULL, SectionName=NULL, SubsectionName=NULL,
Headline.contains=NULL, Snippet.contains=NULL, Abstract.contains=NULL,
Headline.pfx=NULL, NewsDesk.nb=NULL, .clusterid=NULL, myCategory=NULL,
perl=FALSE) {
tmp_entity_df <- glb_allobs_df
# Does not work for Popular == NAs ???
if (!is.null(Popular)) {
if (is.na(Popular))
tmp_entity_df <- tmp_entity_df[is.na(tmp_entity_df$Popular), ] else
tmp_entity_df <- tmp_entity_df[tmp_entity_df$Popular == Popular, ]
}
if (!is.null(NewsDesk))
tmp_entity_df <- tmp_entity_df[tmp_entity_df$NewsDesk == NewsDesk, ]
if (!is.null(SectionName))
tmp_entity_df <- tmp_entity_df[tmp_entity_df$SectionName == SectionName, ]
if (!is.null(SubsectionName))
tmp_entity_df <- tmp_entity_df[tmp_entity_df$SubsectionName == SubsectionName, ]
if (!is.null(Headline.contains))
tmp_entity_df <-
tmp_entity_df[grep(Headline.contains, tmp_entity_df$Headline, perl=perl), ]
if (!is.null(Snippet.contains))
tmp_entity_df <-
tmp_entity_df[grep(Snippet.contains, tmp_entity_df$Snippet, perl=perl), ]
if (!is.null(Abstract.contains))
tmp_entity_df <-
tmp_entity_df[grep(Abstract.contains, tmp_entity_df$Abstract, perl=perl), ]
if (!is.null(Headline.pfx)) {
if (length(grep("Headline.pfx", names(tmp_entity_df), fixed=TRUE, value=TRUE))
> 0) tmp_entity_df <-
tmp_entity_df[tmp_entity_df$Headline.pfx == Headline.pfx, ] else
warning("glb_allobs_df does not contain Headline.pfx; ignoring that filter")
}
if (!is.null(NewsDesk.nb)) {
if (any(grepl("NewsDesk.nb", names(tmp_entity_df), fixed=TRUE)) > 0)
tmp_entity_df <-
tmp_entity_df[tmp_entity_df$NewsDesk.nb == NewsDesk.nb, ] else
warning("glb_allobs_df does not contain NewsDesk.nb; ignoring that filter")
}
if (!is.null(.clusterid)) {
if (any(grepl(".clusterid", names(tmp_entity_df), fixed=TRUE)) > 0)
tmp_entity_df <-
tmp_entity_df[tmp_entity_df$.clusterid == .clusterid, ] else
warning("glb_allobs_df does not contain .clusterid; ignoring that filter")
}
if (!is.null(myCategory)) {
if (any(grepl("myCategory", names(tmp_entity_df), fixed=TRUE)) > 0)
tmp_entity_df <-
tmp_entity_df[tmp_entity_df$myCategory == myCategory, ] else
warning("glb_allobs_df does not contain myCategory; ignoring that filter")
}
return(glb_allobs_df$UniqueID %in% tmp_entity_df$UniqueID)
}
dsp_obs <- function(..., cols=c(NULL), all=FALSE) {
tmp_df <- glb_allobs_df[sel_obs(...),
union(c("UniqueID", "Popular", "myCategory", "Headline"), cols), FALSE]
if(all) { print(tmp_df) } else { myprint_df(tmp_df) }
}
#dsp_obs(Popular=1, NewsDesk="", SectionName="", Headline.contains="Boehner")
# dsp_obs(Popular=1, NewsDesk="", SectionName="")
# dsp_obs(Popular=NA, NewsDesk="", SectionName="")
dsp_tbl <- function(...) {
tmp_entity_df <- glb_allobs_df[sel_obs(...), ]
tmp_tbl <- table(tmp_entity_df$NewsDesk,
tmp_entity_df$SectionName,
tmp_entity_df$SubsectionName,
tmp_entity_df$Popular, useNA="ifany")
#print(names(tmp_tbl))
#print(dimnames(tmp_tbl))
print(tmp_tbl)
}
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Create myCategory <- NewsDesk#SectionName#SubsectionName
# Fix some data before merging categories
glb_allobs_df[sel_obs(Headline.contains="Your Turn:", NewsDesk=""),
"NewsDesk"] <- "Styles"
glb_allobs_df[sel_obs(Headline.contains="School", NewsDesk="", SectionName="U.S.",
SubsectionName=""),
"SubsectionName"] <- "Education"
glb_allobs_df[sel_obs(Headline.contains="Today in Small Business:", NewsDesk="Business"),
"SectionName"] <- "Business Day"
glb_allobs_df[sel_obs(Headline.contains="Today in Small Business:", NewsDesk="Business"),
"SubsectionName"] <- "Small Business"
glb_allobs_df[sel_obs(Headline.contains="Readers Respond:"),
"SectionName"] <- "Opinion"
glb_allobs_df[sel_obs(Headline.contains="Readers Respond:"),
"SubsectionName"] <- "Room For Debate"
# glb_allobs_df[sel_obs(NewsDesk="Business", SectionName="", SubsectionName="", Popular=NA),
# "SubsectionName"] <- "Small Business"
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(7973),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df[sel_obs(NewsDesk="Business", SectionName="", SubsectionName=""),
# "SectionName"] <- "Technology"
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(5076, 5736, 5924, 5911, 6532),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df[sel_obs(SectionName="Health"),
# "NewsDesk"] <- "Science"
# glb_allobs_df[sel_obs(SectionName="Travel"),
# "NewsDesk"] <- "Travel"
#
# glb_allobs_df[sel_obs(SubsectionName="Fashion & Style"),
# "SectionName"] <- ""
# glb_allobs_df[sel_obs(SubsectionName="Fashion & Style"),
# "SubsectionName"] <- ""
# glb_allobs_df[sel_obs(NewsDesk="Styles", SectionName="", SubsectionName="", Popular=1),
# "SectionName"] <- "U.S."
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(5486),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
glb_allobs_df$myCategory <- paste(glb_allobs_df$NewsDesk,
glb_allobs_df$SectionName,
glb_allobs_df$SubsectionName,
sep="#")
dsp_obs( Headline.contains="Music:"
#,NewsDesk=""
#,SectionName=""
#,SubsectionName="Fashion & Style"
#,Popular=1 #NA
,cols= c("UniqueID", "Headline", "Popular", "myCategory",
"NewsDesk", "SectionName", "SubsectionName"),
all=TRUE)## UniqueID Popular myCategory
## 305 305 0 OpEd#Opinion#
## 844 844 1 OpEd#Opinion#
## 1331 1331 0 OpEd#Opinion#
## 1974 1974 0 OpEd#Opinion#
## 2563 2563 0 OpEd#Opinion#
## 3091 3091 0 OpEd#Opinion#
## 3589 3589 0 OpEd#Opinion#
## 4631 4631 0 OpEd#Opinion#
## 5125 5125 0 OpEd#Opinion#
## 5630 5630 0 OpEd#Opinion#
## 6095 6095 0 OpEd#Opinion#
## 6513 6513 1 OpEd#Opinion#
## 6927 6927 NA OpEd#Opinion#
## 7473 7473 NA #Opinion#
## 7931 7931 NA OpEd#Opinion#
## 8217 8217 NA OpEd#Opinion#
## Headline NewsDesk
## 305 Friday Night Music: Lucius Covers John Lennon OpEd
## 844 Friday Night Music: Cheryl Wheeler OpEd
## 1331 Friday Night Music: Cheryl Wheeler, Summer Fly OpEd
## 1974 Friday Night Music: Quilt OpEd
## 2563 Friday Night Music: Lucius in Asheville OpEd
## 3091 Friday Night Music: Sarah Jarosz and the Milk Carton Kids OpEd
## 3589 Friday Night Music: Lucius Covers the Kinks OpEd
## 4631 Friday Night Music: Amason OpEd
## 5125 Friday Night Music: Suzanne Vega, Jacob and the Angel OpEd
## 5630 Friday Night Music: Suzanne Vega, I Never Wear White OpEd
## 6095 Friday Night Music: Jessica Hernandez and the Deltas OpEd
## 6513 Saturday Morning Music: Stay Gold OpEd
## 6927 Friday Night Music: Lucius, Monsters OpEd
## 7473 Friday Night Music: Peter Gabriel, 1993
## 7931 Friday Night Music: The Roches, Winter Wonderland OpEd
## 8217 Friday Night Music: Sarah Jarosz and Aoife O'Donovan OpEd
## SectionName SubsectionName
## 305 Opinion
## 844 Opinion
## 1331 Opinion
## 1974 Opinion
## 2563 Opinion
## 3091 Opinion
## 3589 Opinion
## 4631 Opinion
## 5125 Opinion
## 5630 Opinion
## 6095 Opinion
## 6513 Opinion
## 6927 Opinion
## 7473 Opinion
## 7931 Opinion
## 8217 Opiniondsp_obs( Headline.contains="."
,NewsDesk=""
,SectionName="Opinion"
,SubsectionName=""
#,Popular=1 #NA
,cols= c("UniqueID", "Headline", "Popular", "myCategory",
"NewsDesk", "SectionName", "SubsectionName"),
all=TRUE)## UniqueID Popular myCategory
## 516 516 0 #Opinion#
## 918 918 0 #Opinion#
## 7473 7473 NA #Opinion#
## 7445 7445 NA #Opinion#
## 7419 7419 NA #Opinion#
## 7505 7505 NA #Opinion#
## 7509 7509 NA #Opinion#
## Headline
## 516 This Is Life Among the Roma, Europes Forgotten People
## 918 What Might Happen If Iran Becomes America's Covert Ally?
## 7473 Friday Night Music: Peter Gabriel, 1993
## 7445 Senate Committee Bothered to Authorize War Against Islamic State
## 7419 Joe on WNYCs Money Talking
## 7505 Rev. Dr. William Barber II on Todays Protest Movements
## 7509 Did Salaita Cross the Line of Civility?
## NewsDesk SectionName SubsectionName
## 516 Opinion
## 918 Opinion
## 7473 Opinion
## 7445 Opinion
## 7419 Opinion
## 7505 Opinion
## 7509 Opinion# Merge some categories
glb_allobs_df$myCategory <-
plyr::revalue(glb_allobs_df$myCategory, c(
"#Business Day#Dealbook" = "Business#Business Day#Dealbook",
"#Business Day#Small Business" = "Business#Business Day#Small Business",
"#Crosswords/Games#" = "Business#Crosswords/Games#",
"Business##" = "Business#Technology#",
"#Open#" = "Business#Technology#",
"#Technology#" = "Business#Technology#",
"#Arts#" = "Culture#Arts#",
"Culture##" = "Culture#Arts#",
"#World#Asia Pacific" = "Foreign#World#Asia Pacific",
"Foreign##" = "Foreign#World#",
"#N.Y. / Region#" = "Metro#N.Y. / Region#",
"#Opinion#" = "OpEd#Opinion#",
"OpEd##" = "OpEd#Opinion#",
"#Health#" = "Science#Health#",
"Science##" = "Science#Health#",
"Styles##" = "Styles##Fashion",
"Styles#Health#" = "Science#Health#",
"Styles#Style#Fashion & Style" = "Styles##Fashion",
"#Travel#" = "Travel#Travel#",
"Magazine#Magazine#" = "myOther",
"National##" = "myOther",
"National#U.S.#Politics" = "myOther",
"Sports##" = "myOther",
"Sports#Sports#" = "myOther",
"#U.S.#" = "myOther",
# "Business##Small Business" = "Business#Business Day#Small Business",
#
# "#Opinion#" = "#Opinion#Room For Debate",
"##" = "##"
# "Business##" = "Business#Business Day#Dealbook",
# "Foreign#World#" = "Foreign##",
# "#Open#" = "Other",
# "#Opinion#The Public Editor" = "OpEd#Opinion#",
# "Styles#Health#" = "Styles##",
# "Styles#Style#Fashion & Style" = "Styles##",
# "#U.S.#" = "#U.S.#Education",
))
ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
mycreate_sqlxtab_df(glb_allobs_df,
c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
myCategory + NewsDesk + SectionName + SubsectionName ~
Popular.fctr, sum, value.var=".n"))
myprint_df(ctgry_cast_df)## myCategory NewsDesk SectionName SubsectionName
## 33 OpEd#Opinion# OpEd Opinion
## 36 Science#Health# Science Health
## 1 ##
## 11 Business#Crosswords/Games# Business Crosswords/Games
## 40 Styles#U.S.# Styles U.S.
## 7 Business#Business Day#Dealbook Business Business Day Dealbook
## N Y NA
## 33 113 407 141
## 36 73 119 55
## 1 1163 110 338
## 11 19 103 38
## 40 77 100 62
## 7 864 88 291
## myCategory NewsDesk SectionName
## 35 Science#Health# Science
## 17 Culture#Arts# Culture
## 16 Culture#Arts# Arts
## 8 Business#Business Day#Small Business Business Day
## 13 Business#Technology# Technology
## 28 myOther National U.S.
## SubsectionName N Y NA
## 35 0 2 2
## 17 1 0 70
## 16 0 0 11
## 8 Small Business 1 0 4
## 13 0 0 1
## 28 Politics 2 0 0
## myCategory NewsDesk SectionName SubsectionName N Y NA
## 27 myOther National 2 0 0
## 28 myOther National U.S. Politics 2 0 0
## 29 myOther Sports 1 0 0
## 30 myOther Sports Sports 1 0 0
## 37 Science#Health# Styles Health 1 0 0
## 39 Styles##Fashion Styles Style Fashion & Style 2 0 0write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
row.names=FALSE)
print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
useNA="ifany"))##
## N Y <NA>
## ## 1163 110 338
## #Multimedia# 139 2 52
## #Opinion#Room For Debate 69 7 24
## #Opinion#The Public Editor 4 16 10
## #U.S.#Education 325 0 90
## Business#Business Day#Dealbook 864 88 304
## Business#Business Day#Small Business 135 5 42
## Business#Crosswords/Games# 20 103 42
## Business#Technology# 288 51 113
## Culture#Arts# 626 50 244
## Foreign#World# 172 0 47
## Foreign#World#Asia Pacific 200 3 56
## Metro#N.Y. / Region# 181 17 67
## myOther 38 0 3
## OpEd#Opinion# 115 408 164
## Science#Health# 74 122 57
## Styles##Fashion 118 1 15
## Styles#U.S.# 77 100 62
## Travel#Travel# 116 1 35
## TStyle## 715 9 105dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_vars, "Popular")],
sel_df[, c(glb_id_vars, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c("myCategory", "NewsDesk", "SectionName", "SubsectionName"))
# Copy Headline into Snipper & Abstract if they are empty
print(glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, c("Headline", "Snippet")])## Headline
## 2838 First Draft Focus: Off to Raise Money for Democrats
## 3728 Verbatim: Obama as Supreme Court Justice?
## 4904 Election 2014: Live Coverage
## 4994 Election 2014: Live Coverage
## 5065 First Draft Focus: Honoring a Civil War Hero
## 5029 First Draft Focus: Perry's Day in Court
## 5160 Supreme Court to Hear New Health Law Challenge
## 5254 Verbatim: Will Rick Perry Run?
## 5472 First Draft Focus: A Red Carpet Welcome
## 7164 Does Torture Work? C.I.A.'s Claims vs. Senate Panel's Findings
## 7129 First Draft Focus: Pass a Bill
## 7368 Verbatim: The People's Priorities
## 7364 First Draft Focus: Three Wise Men
## Snippet
## 2838
## 3728
## 4904
## 4994
## 5065
## 5029
## 5160
## 5254
## 5472
## 7164
## 7129
## 7368
## 7364print(glb_allobs_df[glb_allobs_df$Headline == glb_allobs_df$Snippet,
c("UniqueID", "Headline", "Snippet")])## [1] UniqueID Headline Snippet
## <0 rows> (or 0-length row.names)glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, "Snippet"] <-
glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, "Headline"]
print(glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, c("Headline", "Abstract")])## Headline
## 2838 First Draft Focus: Off to Raise Money for Democrats
## 3728 Verbatim: Obama as Supreme Court Justice?
## 4904 Election 2014: Live Coverage
## 4994 Election 2014: Live Coverage
## 5065 First Draft Focus: Honoring a Civil War Hero
## 5029 First Draft Focus: Perry's Day in Court
## 5160 Supreme Court to Hear New Health Law Challenge
## 5254 Verbatim: Will Rick Perry Run?
## 5472 First Draft Focus: A Red Carpet Welcome
## 7164 Does Torture Work? C.I.A.'s Claims vs. Senate Panel's Findings
## 7129 First Draft Focus: Pass a Bill
## 7368 Verbatim: The People's Priorities
## 7364 First Draft Focus: Three Wise Men
## 7329 Obama Works the Phones to Get Funding Deal Done
## 7315 House Democrats Vent Frustration With White House
## 7310 Funding Bill Hangs in Balance as House Votes
## 7309 Spending Bill Passes House With Democratic Support
## Abstract
## 2838
## 3728
## 4904
## 4994
## 5065
## 5029
## 5160
## 5254
## 5472
## 7164
## 7129
## 7368
## 7364
## 7329
## 7315
## 7310
## 7309print(glb_allobs_df[glb_allobs_df$Headline == glb_allobs_df$Abstract,
c("UniqueID", "Headline", "Abstract")])## [1] UniqueID Headline Abstract
## <0 rows> (or 0-length row.names)glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, "Abstract"] <-
glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, "Headline"]
# WordCount_0_df <- subset(glb_allobs_df, WordCount == 0)
# table(WordCount_0_df$Popular, WordCount_0_df$WordCount, useNA="ifany")
# myprint_df(WordCount_0_df[,
# c("UniqueID", "Popular", "WordCount", "Headline")])
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 3 cleanse.data 2 1 30.891 35.434 4.543
## 4 manage.missing.data 2 2 35.435 NA NAStep 2.2: manage missing data
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
dsp_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## WordCount Popular
## 0 1870
## UniqueID Popular.fctr
## 0 1870
## PubDate.POSIX PubDate.year.fctr
## 0 0
## PubDate.month.fctr PubDate.date.fctr
## 0 0
## PubDate.wkday.fctr PubDate.wkend
## 0 0
## PubDate.hour.fctr PubDate.minute.fctr
## 0 0
## PubDate.second.fctr PubDate.day.minutes.poly.1
## 0 0
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.3
## 0 0
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.5
## 0 0
## PubDate.zoo PubDate.last1
## 0 0
## PubDate.last1.log PubDate.last10
## 0 0
## PubDate.last10.log PubDate.last100
## 0 0
## PubDate.last100.log WordCount.log
## 0 109
## .rnorm myCategory.fctr
## 0 0
## [1] "numeric data w/ 0s in : "
## WordCount Popular
## 109 5439
## UniqueID Popular.fctr
## 0 0
## PubDate.POSIX PubDate.year.fctr
## 0 0
## PubDate.month.fctr PubDate.date.fctr
## 0 0
## PubDate.wkday.fctr PubDate.wkend
## 378 7624
## PubDate.hour.fctr PubDate.minute.fctr
## 0 0
## PubDate.second.fctr PubDate.day.minutes.poly.1
## 0 0
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.3
## 0 0
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.5
## 0 0
## PubDate.zoo PubDate.last1
## 0 11
## PubDate.last1.log PubDate.last10
## 11 10
## PubDate.last10.log PubDate.last100
## 10 100
## PubDate.last100.log WordCount.log
## 100 0
## .rnorm myCategory.fctr
## 0 0
## [1] "numeric data w/ Infs in : "
## WordCount Popular
## 0 0
## UniqueID Popular.fctr
## 0 0
## PubDate.POSIX PubDate.year.fctr
## 0 0
## PubDate.month.fctr PubDate.date.fctr
## 0 0
## PubDate.wkday.fctr PubDate.wkend
## 0 0
## PubDate.hour.fctr PubDate.minute.fctr
## 0 0
## PubDate.second.fctr PubDate.day.minutes.poly.1
## 0 0
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.3
## 0 0
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.5
## 0 0
## PubDate.zoo PubDate.last1
## 0 0
## PubDate.last1.log PubDate.last10
## 0 0
## PubDate.last10.log PubDate.last100
## 0 0
## PubDate.last100.log WordCount.log
## 0 0
## .rnorm myCategory.fctr
## 0 0
## [1] "numeric data w/ NaNs in : "
## WordCount Popular
## 0 0
## UniqueID Popular.fctr
## 0 0
## PubDate.POSIX PubDate.year.fctr
## 0 0
## PubDate.month.fctr PubDate.date.fctr
## 0 0
## PubDate.wkday.fctr PubDate.wkend
## 0 0
## PubDate.hour.fctr PubDate.minute.fctr
## 0 0
## PubDate.second.fctr PubDate.day.minutes.poly.1
## 0 0
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.3
## 0 0
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.5
## 0 0
## PubDate.zoo PubDate.last1
## 0 0
## PubDate.last1.log PubDate.last10
## 0 0
## PubDate.last10.log PubDate.last100
## 0 0
## PubDate.last100.log WordCount.log
## 0 0
## .rnorm myCategory.fctr
## 0 0
## [1] "string data missing in : "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2407 2883 6156 0 0
## Abstract PubDate myCategory
## 0 0 0# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
return(out_impent_df[, "WordCount.log"])
}
if (glb_impute_na_data)
glb_allobs_df[, "WordCount.log"] <- glb_impute_missing_data()## Loading required package: mice
## Loading required package: Rcpp
## mice 2.22 2014-06-10## [1] "Summary before imputation: "
## PubDate.year.fctr PubDate.date.fctr PubDate.wkday.fctr PubDate.wkend
## 2014:8402 (0.97,7]:1981 0: 378 Min. :0.0000
## (7,13] :1757 1:1605 1st Qu.:0.0000
## (13,19] :1808 2:1559 Median :0.0000
## (19,25] :1650 3:1614 Mean :0.0926
## (25,31] :1206 4:1539 3rd Qu.:0.0000
## 5:1470 Max. :1.0000
## 6: 237
## PubDate.hour.fctr PubDate.minute.fctr PubDate.second.fctr
## (-0.023,7.67]:1610 (-0.059,14.8]:3119 (-0.059,14.8]:2134
## (7.67,15.3] :4484 (14.8,29.5] :1671 (14.8,29.5] :2063
## (15.3,23] :2308 (29.5,44.2] :1995 (29.5,44.2] :2112
## (44.2,59.1] :1617 (44.2,59.1] :2093
##
##
##
## PubDate.day.minutes.poly.1 PubDate.day.minutes.poly.2
## Min. :-0.0274946 Min. :-0.008759
## 1st Qu.:-0.0078858 1st Qu.:-0.007794
## Median : 0.0002845 Median :-0.003965
## Mean : 0.0000000 Mean : 0.000000
## 3rd Qu.: 0.0080100 3rd Qu.: 0.002979
## Max. : 0.0247592 Max. : 0.042684
##
## PubDate.day.minutes.poly.3 PubDate.day.minutes.poly.4
## Min. :-0.045125 Min. :-0.0183274
## 1st Qu.:-0.007780 1st Qu.:-0.0085001
## Median :-0.000337 Median : 0.0007244
## Mean : 0.000000 Mean : 0.0000000
## 3rd Qu.: 0.007554 3rd Qu.: 0.0081632
## Max. : 0.052153 Max. : 0.0667744
##
## PubDate.day.minutes.poly.5 PubDate.last1.log PubDate.last10.log
## Min. :-0.0245092 Min. : 0.000 Min. : 0.000
## 1st Qu.:-0.0082017 1st Qu.: 5.263 1st Qu.: 8.516
## Median :-0.0003821 Median : 6.292 Median : 8.868
## Mean : 0.0000000 Mean : 6.094 Mean : 9.048
## 3rd Qu.: 0.0077006 3rd Qu.: 7.126 3rd Qu.: 9.424
## Max. : 0.0847176 Max. :10.875 Max. :11.744
##
## PubDate.last100.log WordCount.log .rnorm
## Min. : 0.00 Min. :0.6932 Min. :-3.281785
## 1st Qu.:11.37 1st Qu.:5.2679 1st Qu.:-0.681275
## Median :11.43 Median :5.9480 Median : 0.007735
## Mean :11.49 Mean :5.8263 Mean :-0.000264
## 3rd Qu.:11.78 3rd Qu.:6.6067 3rd Qu.: 0.673409
## Max. :12.95 Max. :9.2977 Max. : 3.987726
## NA's :109
## myCategory.fctr
## ## :1611
## Business#Business Day#Dealbook:1256
## Culture#Arts# : 920
## TStyle## : 829
## OpEd#Opinion# : 687
## Business#Technology# : 452
## (Other) :2647
##
## iter imp variable
## 1 1 WordCount.log
## 1 2 WordCount.log
## 1 3 WordCount.log
## 1 4 WordCount.log
## 1 5 WordCount.log
## 2 1 WordCount.log
## 2 2 WordCount.log
## 2 3 WordCount.log
## 2 4 WordCount.log
## 2 5 WordCount.log
## 3 1 WordCount.log
## 3 2 WordCount.log
## 3 3 WordCount.log
## 3 4 WordCount.log
## 3 5 WordCount.log
## 4 1 WordCount.log
## 4 2 WordCount.log
## 4 3 WordCount.log
## 4 4 WordCount.log
## 4 5 WordCount.log
## 5 1 WordCount.log
## 5 2 WordCount.log
## 5 3 WordCount.log
## 5 4 WordCount.log
## 5 5 WordCount.log
## PubDate.year.fctr PubDate.date.fctr PubDate.wkday.fctr PubDate.wkend
## 2014:8402 (0.97,7]:1981 0: 378 Min. :0.0000
## (7,13] :1757 1:1605 1st Qu.:0.0000
## (13,19] :1808 2:1559 Median :0.0000
## (19,25] :1650 3:1614 Mean :0.0926
## (25,31] :1206 4:1539 3rd Qu.:0.0000
## 5:1470 Max. :1.0000
## 6: 237
## PubDate.hour.fctr PubDate.minute.fctr PubDate.second.fctr
## (-0.023,7.67]:1610 (-0.059,14.8]:3119 (-0.059,14.8]:2134
## (7.67,15.3] :4484 (14.8,29.5] :1671 (14.8,29.5] :2063
## (15.3,23] :2308 (29.5,44.2] :1995 (29.5,44.2] :2112
## (44.2,59.1] :1617 (44.2,59.1] :2093
##
##
##
## PubDate.day.minutes.poly.1 PubDate.day.minutes.poly.2
## Min. :-0.0274946 Min. :-0.008759
## 1st Qu.:-0.0078858 1st Qu.:-0.007794
## Median : 0.0002845 Median :-0.003965
## Mean : 0.0000000 Mean : 0.000000
## 3rd Qu.: 0.0080100 3rd Qu.: 0.002979
## Max. : 0.0247592 Max. : 0.042684
##
## PubDate.day.minutes.poly.3 PubDate.day.minutes.poly.4
## Min. :-0.045125 Min. :-0.0183274
## 1st Qu.:-0.007780 1st Qu.:-0.0085001
## Median :-0.000337 Median : 0.0007244
## Mean : 0.000000 Mean : 0.0000000
## 3rd Qu.: 0.007554 3rd Qu.: 0.0081632
## Max. : 0.052153 Max. : 0.0667744
##
## PubDate.day.minutes.poly.5 PubDate.last1.log PubDate.last10.log
## Min. :-0.0245092 Min. : 0.000 Min. : 0.000
## 1st Qu.:-0.0082017 1st Qu.: 5.263 1st Qu.: 8.516
## Median :-0.0003821 Median : 6.292 Median : 8.868
## Mean : 0.0000000 Mean : 6.094 Mean : 9.048
## 3rd Qu.: 0.0077006 3rd Qu.: 7.126 3rd Qu.: 9.424
## Max. : 0.0847176 Max. :10.875 Max. :11.744
##
## PubDate.last100.log WordCount.log .rnorm
## Min. : 0.00 Min. :0.6931 Min. :-3.281785
## 1st Qu.:11.37 1st Qu.:5.2730 1st Qu.:-0.681275
## Median :11.43 Median :5.9480 Median : 0.007735
## Mean :11.49 Mean :5.8273 Mean :-0.000264
## 3rd Qu.:11.78 3rd Qu.:6.6080 3rd Qu.: 0.673409
## Max. :12.95 Max. :9.2977 Max. : 3.987726
##
## myCategory.fctr
## ## :1611
## Business#Business Day#Dealbook:1256
## Culture#Arts# : 920
## TStyle## : 829
## OpEd#Opinion# : 687
## Business#Technology# : 452
## (Other) :2647dsp_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## WordCount Popular
## 0 1870
## UniqueID Popular.fctr
## 0 1870
## PubDate.POSIX PubDate.year.fctr
## 0 0
## PubDate.month.fctr PubDate.date.fctr
## 0 0
## PubDate.wkday.fctr PubDate.wkend
## 0 0
## PubDate.hour.fctr PubDate.minute.fctr
## 0 0
## PubDate.second.fctr PubDate.day.minutes.poly.1
## 0 0
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.3
## 0 0
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.5
## 0 0
## PubDate.zoo PubDate.last1
## 0 0
## PubDate.last1.log PubDate.last10
## 0 0
## PubDate.last10.log PubDate.last100
## 0 0
## PubDate.last100.log WordCount.log
## 0 0
## .rnorm myCategory.fctr
## 0 0
## [1] "numeric data w/ 0s in : "
## WordCount Popular
## 109 5439
## UniqueID Popular.fctr
## 0 0
## PubDate.POSIX PubDate.year.fctr
## 0 0
## PubDate.month.fctr PubDate.date.fctr
## 0 0
## PubDate.wkday.fctr PubDate.wkend
## 378 7624
## PubDate.hour.fctr PubDate.minute.fctr
## 0 0
## PubDate.second.fctr PubDate.day.minutes.poly.1
## 0 0
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.3
## 0 0
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.5
## 0 0
## PubDate.zoo PubDate.last1
## 0 11
## PubDate.last1.log PubDate.last10
## 11 10
## PubDate.last10.log PubDate.last100
## 10 100
## PubDate.last100.log WordCount.log
## 100 0
## .rnorm myCategory.fctr
## 0 0
## [1] "numeric data w/ Infs in : "
## WordCount Popular
## 0 0
## UniqueID Popular.fctr
## 0 0
## PubDate.POSIX PubDate.year.fctr
## 0 0
## PubDate.month.fctr PubDate.date.fctr
## 0 0
## PubDate.wkday.fctr PubDate.wkend
## 0 0
## PubDate.hour.fctr PubDate.minute.fctr
## 0 0
## PubDate.second.fctr PubDate.day.minutes.poly.1
## 0 0
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.3
## 0 0
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.5
## 0 0
## PubDate.zoo PubDate.last1
## 0 0
## PubDate.last1.log PubDate.last10
## 0 0
## PubDate.last10.log PubDate.last100
## 0 0
## PubDate.last100.log WordCount.log
## 0 0
## .rnorm myCategory.fctr
## 0 0
## [1] "numeric data w/ NaNs in : "
## WordCount Popular
## 0 0
## UniqueID Popular.fctr
## 0 0
## PubDate.POSIX PubDate.year.fctr
## 0 0
## PubDate.month.fctr PubDate.date.fctr
## 0 0
## PubDate.wkday.fctr PubDate.wkend
## 0 0
## PubDate.hour.fctr PubDate.minute.fctr
## 0 0
## PubDate.second.fctr PubDate.day.minutes.poly.1
## 0 0
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.3
## 0 0
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.5
## 0 0
## PubDate.zoo PubDate.last1
## 0 0
## PubDate.last1.log PubDate.last10
## 0 0
## PubDate.last10.log PubDate.last100
## 0 0
## PubDate.last100.log WordCount.log
## 0 0
## .rnorm myCategory.fctr
## 0 0
## [1] "string data missing in : "
## NewsDesk SectionName SubsectionName Headline Snippet
## 2407 2883 6156 0 0
## Abstract PubDate myCategory
## 0 0 0glb_chunks_df <- myadd_chunk(glb_chunks_df, "encode.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 4 manage.missing.data 2 2 35.435 42.372 6.938
## 5 encode.data 2 3 42.373 NA NAStep 2.3: encode data
# map_<col_name>_df <- myimport_data(
# url="<map_url>",
# comment="map_<col_name>_df", print_diagn=TRUE)
# map_<col_name>_df <- read.csv(paste0(getwd(), "/data/<file_name>.csv"), strip.white=TRUE)
# glb_trnobs_df <- mymap_codes(glb_trnobs_df, "<from_col_name>", "<to_col_name>",
# map_<to_col_name>_df, map_join_col_name="<map_join_col_name>",
# map_tgt_col_name="<to_col_name>")
# glb_newobs_df <- mymap_codes(glb_newobs_df, "<from_col_name>", "<to_col_name>",
# map_<to_col_name>_df, map_join_col_name="<map_join_col_name>",
# map_tgt_col_name="<to_col_name>")
# glb_trnobs_df$<col_name>.fctr <- factor(glb_trnobs_df$<col_name>,
# as.factor(union(glb_trnobs_df$<col_name>, glb_newobs_df$<col_name>)))
# glb_newobs_df$<col_name>.fctr <- factor(glb_newobs_df$<col_name>,
# as.factor(union(glb_trnobs_df$<col_name>, glb_newobs_df$<col_name>)))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 encode.data 2 3 42.373 42.403 0.031
## 6 extract.features 3 0 42.404 NA NAStep 3.0: extract features
#```{r extract_features, cache=FALSE, eval=glb_is_textual}
extract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor bgn end elapsed
## 1 extract.features_bgn 1 0 42.453 NA NA# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor bgn end
## 1 extract.features_bgn 1 0 42.453 42.465
## 2 extract.features_factorize.str.vars 2 0 42.466 NA
## elapsed
## 1 0.013
## 2 NAprint(str_vars <- myfind_chr_cols_df(glb_allobs_df))## NewsDesk SectionName SubsectionName Headline
## "NewsDesk" "SectionName" "SubsectionName" "Headline"
## Snippet Abstract PubDate .src
## "Snippet" "Abstract" "PubDate" ".src"
## myCategory
## "myCategory"if (length(str_vars <- setdiff(str_vars,
glb_exclude_vars_as_features)) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <- factor(glb_allobs_df[, var],
as.factor(unique(glb_allobs_df[, var])))
# glb_trnobs_df[, paste0(var, ".fctr")] <- factor(glb_trnobs_df[, var],
# as.factor(unique(glb_allobs_df[, var])))
# glb_newobs_df[, paste0(var, ".fctr")] <- factor(glb_newobs_df[, var],
# as.factor(unique(glb_allobs_df[, var])))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}
if (glb_is_textual) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(re_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(re_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
#tmp_freq_df <- chk_pattern_freq("\\bNew (\\w)+", ignore.case=FALSE)
#subset(chk_pattern_freq("\\bNew (\\w)+", ignore.case=FALSE), grepl("New [[:upper:]]", pattern))
#chk_pattern_freq("\\bnew (\\W)+")
chk_subfn <- function(pos_ix) {
re_str <- gsubfn_args_lst[["re_str"]][[pos_ix]]
print("re_str:"); print(re_str)
rp_frmla <- gsubfn_args_lst[["rp_frmla"]][[pos_ix]]
print("rp_frmla:"); print(rp_frmla, showEnv=FALSE)
tmp_vctr <- grep(re_str, txt_vctr, value=TRUE, ignore.case=TRUE)[1:5]
print("Before:")
print(tmp_vctr)
print("After:")
print(gsubfn(re_str, rp_frmla, tmp_vctr, ignore.case=TRUE))
}
#chk_subfn(1)
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end], glb_txt_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_lst; all.equal(sav_txt_lst, glb_txt_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_lst[["Headline"]][1:4200])
#all.equal(sav_txt_lst[["Headline"]][1:2000], glb_txt_lst[["Headline"]][1:2000])
#all.equal(sav_txt_lst[["Headline"]][1:1000], glb_txt_lst[["Headline"]][1:1000])
#all.equal(sav_txt_lst[["Headline"]][1:500], glb_txt_lst[["Headline"]][1:500])
#all.equal(sav_txt_lst[["Headline"]][1:200], glb_txt_lst[["Headline"]][1:200])
#all.equal(sav_txt_lst[["Headline"]][1:100], glb_txt_lst[["Headline"]][1:100])
#chk.equal( 1, 100)
#chk.equal(51, 100)
#chk.equal(81, 100)
#chk.equal(81, 90)
#chk.equal(81, 85)
#chk.equal(86, 90)
#chk.equal(96, 100)
#dsp.equal(86, 90)
glb_txt_map_df <- read.csv("mytxt_map.csv", comment.char="#", strip.white=TRUE)
glb_txt_lst <- list();
print(sprintf("Building glb_txt_lst..."))
glb_txt_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_vctr <- glb_allobs_df[, txt_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glb_txt_vars[3]; txt_vctr <- glb_txt_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_lst) <- glb_txt_vars
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(tmp_vctr <- grep("[[:upper:]]\\.", txt_vctr, value=TRUE, ignore.case=FALSE))
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, tolower)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument)
txt_corpus <- tm_map(txt_corpus, removePunctuation,
preserve_intra_word_dashes=TRUE)
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full, full_Tf_df)
cmpnd_Tf_df <- full_Tf_df[grep("-", full_Tf_df$term, value=TRUE) ,]
filter_df <- read.csv("mytxt_compound.csv", comment.char="#", strip.white=TRUE)
cmpnd_Tf_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "term"], ignore.case=TRUE)
cmpnd_Tf_df <- subset(cmpnd_Tf_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_Tf_df): %d", nrow(cmpnd_Tf_df)))
myprint_df(cmpnd_Tf_df)
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining compound terms in %s: ", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
# find_cmpnd_wrds(txt_vctr)
#grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
glb_corpus_lst <- list()
print(sprintf("Building glb_corpus_lst..."))
glb_corpus_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_corpus <- Corpus(VectorSource(glb_txt_lst[[txt_var]]))
txt_corpus <- tm_map(txt_corpus, tolower) #nuppr
txt_corpus <- tm_map(txt_corpus, PlainTextDocument)
txt_corpus <- tm_map(txt_corpus, removePunctuation) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
# Not to be run in production
inspect_terms <- function() {
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full +term, full_Tf_df)
print(myplot_histogram(full_Tf_df, "Tf.full"))
myprint_df(full_Tf_df)
#txt_corpus[[which(grepl("zun", txt_vctr, ignore.case=TRUE))]]
digit_terms_df <- subset(full_Tf_df, grepl("[[:digit:]]", term))
myprint_df(digit_terms_df)
return(full_Tf_df)
}
#print("RemovePunct:"); remove_punct_Tf_df <- inspect_terms()
txt_corpus <- tm_map(txt_corpus, removeWords,
c(glb_append_stop_words[[txt_var]],
stopwords("english"))) #nstopwrds
#print("StoppedWords:"); stopped_words_Tf_df <- inspect_terms()
txt_corpus <- tm_map(txt_corpus, stemDocument) #Features for lost information ???
#txt_corpus <- tm_map(txt_corpus, content_transformer(stemDocument))
#print("StemmedWords:"); stemmed_words_Tf_df <- inspect_terms()
#stemmed_stopped_Tf_df <- merge(stemmed_words_Tf_df, stopped_words_Tf_df, by="term", all=TRUE, suffixes=c(".stem", ".stop"))
#myprint_df(stemmed_stopped_Tf_df)
#print(subset(stemmed_stopped_Tf_df, grepl("compan", term)))
#glb_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_corpus_lst) <- glb_txt_vars
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Extracting TfIDf terms for %s...", txt_var))
txt_corpus <- glb_corpus_lst[[txt_var]]
# full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
# control=list(weighting=weightTf))
full_TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
sprs_TfIdf_DTM <- removeSparseTerms(full_TfIdf_DTM,
glb_sprs_thresholds[txt_var])
# glb_full_DTM_lst[[txt_var]] <- full_Tf_DTM
# glb_sprs_DTM_lst[[txt_var]] <- sprs_Tf_DTM
glb_full_DTM_lst[[txt_var]] <- full_TfIdf_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_TfIdf_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Reporting TfIDf terms for %s...", txt_var))
full_TfIdf_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_TfIdf_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_TfIdf_DTM)
full_TfIdf_mtrx <- as.matrix(full_TfIdf_DTM)
rownames(full_TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_TfIdf_vctr <- colSums(full_TfIdf_mtrx)
names(full_TfIdf_vctr) <- dimnames(full_TfIdf_DTM)[[2]]
#grep("scene", names(full_TfIdf_vctr), value=TRUE)
#which.max(full_TfIdf_mtrx[, "yearlong"])
full_TfIdf_df <- as.data.frame(full_TfIdf_vctr)
names(full_TfIdf_df) <- "TfIdf.full"
full_TfIdf_df$term <- rownames(full_TfIdf_df)
full_TfIdf_df$freq.full <- colSums(full_TfIdf_mtrx != 0)
full_TfIdf_df <- orderBy(~ -TfIdf.full, full_TfIdf_df)
print(" Sparse TermMatrix:"); print(sprs_TfIdf_DTM)
sprs_TfIdf_vctr <- colSums(as.matrix(sprs_TfIdf_DTM))
names(sprs_TfIdf_vctr) <- dimnames(sprs_TfIdf_DTM)[[2]]
sprs_TfIdf_df <- as.data.frame(sprs_TfIdf_vctr)
names(sprs_TfIdf_df) <- "TfIdf.sprs"
sprs_TfIdf_df$term <- rownames(sprs_TfIdf_df)
sprs_TfIdf_df$freq.sprs <- colSums(as.matrix(sprs_TfIdf_DTM) != 0)
sprs_TfIdf_df <- orderBy(~ -TfIdf.sprs, sprs_TfIdf_df)
terms_TfIdf_df <- merge(full_TfIdf_df, sprs_TfIdf_df, all.x=TRUE)
terms_TfIdf_df$in.sprs <- !is.na(terms_TfIdf_df$freq.sprs)
plt_TfIdf_df <- subset(terms_TfIdf_df,
TfIdf.full >= min(terms_TfIdf_df$TfIdf.sprs, na.rm=TRUE))
plt_TfIdf_df$label <- ""
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "label"] <-
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"]
glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_TfIdf_df <- orderBy(~ -value, melt(terms_TfIdf_df, id.var="term"))
print(ggplot(melt_TfIdf_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, !is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(melt_TfIdf_df, "term", "value",
colorcol_name="variable"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(head(melt_TfIdf_df, 10), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# sav_sprs_DTM_lst <- glb_sprs_DTM_lst
# print(identical(sav_glb_corpus_lst, glb_corpus_lst))
# print(all.equal(length(sav_glb_corpus_lst), length(glb_corpus_lst)))
# print(all.equal(names(sav_glb_corpus_lst), names(glb_corpus_lst)))
# print(all.equal(sav_glb_corpus_lst[["Headline"]], glb_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
# print(identical(sav_sprs_DTM_lst, glb_sprs_DTM_lst))
rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df, terms_TfIdf_df)
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_X_df)), sep="")
rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
# plt_X_df <- cbind(txt_X_df, glb_allobs_df[, c(glb_id_vars, glb_rsp_var)])
# print(myplot_box(df=plt_X_df, ycol_names="H.T.today", xcol_name=glb_rsp_var))
# log_X_df <- log(1 + txt_X_df)
# colnames(log_X_df) <- paste(colnames(txt_X_df), ".log", sep="")
# plt_X_df <- cbind(log_X_df, glb_allobs_df[, c(glb_id_vars, glb_rsp_var)])
# print(myplot_box(df=plt_X_df, ycol_names="H.T.today.log", xcol_name=glb_rsp_var))
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#sav_allobs_df <- glb_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_vars, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glb_txt_vars, .combine=cbind) %dopar% {
#for (txt_var in glb_txt_vars) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
#txt_X_df <- glb_allobs_df[, c(glb_id_vars, ".rnorm"), FALSE]
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.nwrds.log & .nwrds.unq.log
txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".nwrds.unq.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <-
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")]) - 1)
# Create <txt_var>.nchrs.log
txt_X_df[, paste0(txt_var_pfx, ".nchrs.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".nuppr.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".ndgts.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".npnct", sprintf("%02d", punct_ix), ".log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.nstopwrds.log & <txt_var>ratio.nstopwrds.nwrds
stop_words_rex_str <- paste0("\\b(", paste0(c(glb_append_stop_words[[txt_var]],
stopwords("english")), collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.nstopwrds.nwrds")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".nwrds", ".log")])
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
# Create user-specified pattern vectors
# <txt_var>.P.year.colon
txt_X_df[, paste0(txt_var_pfx, ".P.year.colon")] <-
as.integer(0 + mycount_pattern_occ("[0-9]{4}:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.daily.clip.report")] <-
as.integer(0 + mycount_pattern_occ("Daily Clip Report", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.fashion.week")] <-
as.integer(0 + mycount_pattern_occ("Fashion Week", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.first.draft")] <-
as.integer(0 + mycount_pattern_occ("First Draft", glb_allobs_df[, txt_var]))
#sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
if (txt_var %in% c("Snippet", "Abstract")) {
txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
glb_allobs_df[, txt_var]))
}
#sum(mycount_pattern_occ("[0-9]{4}:", glb_allobs_df$Headline) > 0)
#sum(mycount_pattern_occ("Quandary(.*)(?=:)", glb_allobs_df$Headline, perl=TRUE) > 0)
#sum(mycount_pattern_occ("No Comment(.*):", glb_allobs_df$Headline) > 0)
#sum(mycount_pattern_occ("Friday Night Music:", glb_allobs_df$Headline) > 0)
if (txt_var %in% c("Headline")) {
txt_X_df[, paste0(txt_var_pfx, ".P.facts.figures")] <-
as.integer(0 + mycount_pattern_occ("Facts & Figures:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.friday.night.music")] <-
as.integer(0 + mycount_pattern_occ("Friday Night Music", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.no.comment.colon")] <-
as.integer(0 + mycount_pattern_occ("No Comment(.*):", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.on.this.day")] <-
as.integer(0 + mycount_pattern_occ("On This Day", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.quandary")] <-
as.integer(0 + mycount_pattern_occ("Quandary(.*)(?=:)", glb_allobs_df[, txt_var], perl=TRUE))
txt_X_df[, paste0(txt_var_pfx, ".P.readers.respond")] <-
as.integer(0 + mycount_pattern_occ("Readers Respond", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.recap.colon")] <-
as.integer(0 + mycount_pattern_occ("Recap:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.s.notebook")] <-
as.integer(0 + mycount_pattern_occ("s Notebook", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.today.in.politic")] <-
as.integer(0 + mycount_pattern_occ("Today in Politic", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.today.in.smallbusiness")] <-
as.integer(0 + mycount_pattern_occ("Today in Small Business:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.verbatim.colon")] <-
as.integer(0 + mycount_pattern_occ("Verbatim:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.what.we.are")] <-
as.integer(0 + mycount_pattern_occ("What We're", glb_allobs_df[, txt_var]))
}
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_vars, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
rm(log_X_df, txt_X_df)
}## Loading required package: stringr
## Loading required package: tm
## Loading required package: NLP
##
## Attaching package: 'NLP'
##
## The following object is masked from 'package:ggplot2':
##
## annotate## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 42.466 43.98
## 3 extract.features_process.text 3 0 43.980 NA
## elapsed
## 2 1.514
## 3 NA
## [1] "Building glb_txt_lst..."
## [1] "Remaining Acronyms in Headline:"
## character(0)
## [1] "Remaining Acronyms in Snippet:"
## [1] "In the 1864 election, Gotham delivered nearly twice as many votes to the presidents opponent, George B. McClellan."
## [2] "Researchers are finding more evidence that women who take S.S.R.I. depressants like Prozac and Zoloft increase the likelihood of a variety of health problems in their newborns."
## [3] "Eric T. Schneiderman, NewYorks attorney general, filed a suit on Tuesday that accuses Evans Bank of denying mortgages to African-Americans in Buffalo regardless of their credit."
## [4] "Jeffrey H. Knox, a senior federal prosecutor who butted heads with a number of WallStreet banks, is switching sides."
## [5] "In which Peter A. Collins disproves Aesop."
## [6] "Antonia M. Apps, the lead federal prosecutor in the criminal trial last year of Michael Steinberg, is taking a job with the law firm Milbank, Tweed, Hadley & McCloy."
## [7] "Scholarly musicians prove that Ph.D.s can play."
## [8] "In this new series, Chris Labzda and Bon Duke, the co-founders of the NewYork Fashion Film Festival, curate a short film each week for T. This weeks installment: A collage of classics overlaid with sound from Lauren Wolksteins Social..."
## [9] "I moved to Hudson, N.Y., to slow down and save money. I didnt know Id feel so lonely."
## [10] "A study suggests that exercise can help kids, especially those with A.D.H.D., focus in class."
## [11] "How should the USA respond to the killings of Steven J. Sotloff and James Foley?"
## [12] "Although banks are hiring armies of legal and compliance professionals in response to settlements and fines, they would do better to find ways to streamline their existing legal work, Geoffrey A. Moore and Mark Harris write in an Another View column."
## [13] "William T. Shermans 1864 campaign to take the Georgia city was one of the bloodiest of the Civil War."
## [14] "George Martin, author of the Song of Ice and Fire series, will be promoting his new book at the 92nd St. Y."
## [15] "The workers are OK."
## [16] "The Securities and Exchange Commission has chosen Tracey L. McNeil, an agency veteran and former corporate lawyer, to act as a liaison in resolving problems that retail investors may have with the agency."
## [17] "John A. Paulsons hedge fund suffered across-the-board losses in July and did only marginally better in August, according to an update to investors."
## [18] "Mr. Sheen will direct and perform in Dylan Thomass work he called it a play for voices on Oct. 26 at the 92nd Street Y."
## [19] "The acquisition is the latest move by Jeffrey R. Immelt, the chief of GE, to refocus the conglomerate on its core industrial businesses."
## [20] "The Chinese state news media has cited concerns that the USA is indoctrinating Chinese students by including its founding documents upholding freedom and human rights in the SAT."
## [21] "At the midpoint of the range, the Citizens Financial Group, based in Providence, R.I., would be valued at $13.4 billion."
## [22] "Mark P. Frissoras decision comes a few weeks after Carl C. Icahn disclosed an 8.48 percent stake in the rental car provider."
## [23] "Martin Lipton is virtually guaranteeing the continuation of policies opposed by many NYU faculty members, William D. Cohan writes in the Street Scene column."
## [24] "China Central Television used images of Condoleezza Rice in a report about a Beijing visit by Susan E. Rice, President Obamas national security adviser."
## [25] "If youve never heard the name Lloyd J. Austin, theres a good reason"
## [26] "This weeks video features Ismenia Mendes and David McElwee in a scene from A. R. Gurneys 1977 drama The Wayside Motor Inn."
## [27] "The media and financial data company Thomson Reuters is looking to sell peHUB, Buyouts and Venture Capital Journal, according to a report by peHUB."
## [28] "News headlines briefly flashed that Carl C. Icahn had raised his stake in Gannett, the media company, to nearly 9 percent. But as it turns out, his firm simply made a typo in a regulatory filing."
## [29] "Just two days after her USA Open victory, Serena Williams hosted her first ever fashion show for HSN."
## [30] "Nearly 70 years after they parted, a Chinese veteran is on a quest to find a Japanese woman he met shortly after World War II. His tale offers a warmer recollection of that era."
## [31] "In this lesson we offer a series of topics and questions paired with Times essays, articles, slide shows and videos to help students dig deeper into the causes, effects and overall legacy of World War I."
## [32] "A collection of material related to Rosa Parks, bought last month by Howard G. Buffett, is being lent to the Library of Congress for 10 years."
## [33] "A soldier takes time out of uniform to pursue an M.F.A. in writing, and discovers that what sets him apart, as a veteran at the keyboard, is not as important as the common ground he shares with other writers."
## [34] "Worn by the chef from July 29 to Aug. 4 while cooking at his two restaurants, Blue Hill in Manhattan and Blue Hill at Stone Barns in Pocantico Hills, N.Y."
## [35] "Gov. Andrew M. Cuomo and Mayor Bill de Blasio both enjoyed an Italian sausage at the Feast of SanGennaro in Little Italy on Saturday."
## [36] "Eike Batista was accused of manipulating the share price of his now-bankrupt petroleum company OGX."
## [37] "The activist investor William A. Ackmans new fund, Pershing Square Holdings, aims to list on the the Euronext Amsterdam exchange on Oct. 13."
## [38] "Fraud in the market for penny stocks continues unabated, two criminal cases filed last week show, Peter J. Henning writes in the White Collar Watch column."
## [39] "Quarries and connecting trenches were nothing less than small cities, where the walls bear witness to the people who fought and died in World War I."
## [40] "The deal for Vkontakte ends tensions between the social networks minority shareholders and Alisher B. Usmanov, the Russian billionaire who controls Mail.ru."
## [41] "Sears is borrowing $400 million from its chief, the billionaire Edward S. Lampert, through his hedge fund."
## [42] "KKRs purchase of Pioneers D.J. audio equipment business is the modern-day equivalent of providing gold-rush pickaxes to participants in the rave rush, Jeffrey Goldfarb writes in Reuters Breakingviews."
## [43] "Two grandchildren of Dwight D. Eisenhower say proposed changes by Frank Gehry to his memorial to the former president do not satisfy their concerns."
## [44] "In a letter to Electra Private Equity shareholders, the activist investor Edward J. Bramson said a change in approach in Electras strategy could increase the value of its shares by about $1.6 billion."
## [45] "Prosecutors in Brazil have argued that Mr. Batista profited from insider information when he sold shares in his now bankrupt petroleum company, OGX."
## [46] "Comments by Marshall L. Miller, the No. 2 official in the Justice Departments criminal division, reflect the agencys renewed interest in charging individual bank employees rather than just the banks."
## [47] "The top job at the database giant will be shared by Mark V. Hurd, now co-president, and Safra Catz, who is co-president and chief financial officer."
## [48] "Alibaba is set to start trading on the NewYork Stock Exchange under the ticker symbol BABA. | Markets showed relief that the United Kingdom would not face a tumultuous breakup. | Lawrence J. Ellison announced his retirement as chief executive of..."
## [49] "Average waist circumference but not B.M.I. has increased significantly in the USA, a new study reports."
## [50] "The British private equity firm accused Edward J. Bramsons Sherborne Investors Management of making unverifiable statements and unsubstantiated claims in its effort to reshape Electra."
## [51] "Alibaba soared in its public market debut. | Siemens agreed to buy the Dresser-Rand Group. | EMC weighed a deal with H.P. | Public pension funds may be souring on hedge funds."
## [52] "Venezuela may represent the Latin American and Caribbean nations and sit right next to the USA."
## [53] "Speaker John A. Boehner seemed to blame lazy Americans for the stalled economy."
## [54] "It was unclear whether President Petro O. Poroshenko would travel to NewYork to deliver his address as scheduled on Thursday."
## [55] "As the reclusive genius Richard D. James releases his first album in 13 years, the graphic designer with whom he has frequently collaborated discusses their work together."
## [56] "Vice President Joseph R. Biden Jr. met Carina Castro, the daughter of Julin Castro, secretary of Housing and Urban Development, at a reception honoring Hispanic Heritage Month."
## [57] "Attorney General Eric H. Holder Jr. will announce Tuesday that the federal government will end its fiscal year next week with 4,800 fewer prisoners, the first time since 1980 that the inmate population has declined from year to year."
## [58] "President Obama could end up leaving a smaller imprint on the judiciary than either President Bill Clinton or President George W. Bush."
## [59] "Ever wonder why jewelry stores hide the price tags? How invoice factoring works for small businesses. And why German companies are on a buying spree in the USA."
## [60] "Speaker John A. Boehner urges the new Veterans Affairs secretary to look to the private sector in reviewing how the department delivers health care to veterans."
## [61] "On this day in 1952, Richard M. Nixon gave his famous Checkers speech."
## [62] "To kick off Paris Fashion Week, the chic native whose shirting-based collection is stocked at Barneys NewYork and Dover Street Market shares a list of her favorite places around town with T."
## [63] "Gov. Paul R. LePage of Maine wants to help the NFL address its domestic violence problem."
## [64] "Maurice R. Greenberg, the former chief of AIG, has now raised several million dollars from three WallStreet investors to help cover the cost of the case."
## [65] "Electra said that two influential shareholder advisory services opposed efforts of the activist investor Edward J. Bramson, through his Sherborne Investors Management, to shake up the board of the British private equity firm."
## [66] "Representative James A. Traficant was seriously injured after a tractor accident on his family farm Tuesday night, according to local media reports in Youngstown, Ohio."
## [67] "The role of former President Gerald R. Ford in the filing of a friend-of-the-court brief in a Michigan affirmative action case."
## [68] "The insider trading case against Michael A. Lucarelli, a former executive at Lippert/Heilshorn and Associates, was relatively minor. But he got attention for running barefoot from the court in August."
## [69] "Judge Richard M. Berman of Federal District Court in Manhattan, before sentencing conservative author Dinesh DSouza to probation for violating federal campaign finance laws."
## [70] "Speaker John A. Boehner, an avid golfer, is gearing up for the Ryder Cup, the intense competition between the USA and Europe that begins Friday in Scotland."
## [71] "Speaker John A. Boehner tells First Draft that the new Congress should debate military action in Syria."
## [72] "A new poll has Senator Mark R. Warner, the Democratic incumbent in Virginia, up by 9 points over his Republican opponent."
## [73] "Eric H. Holder Jr.s resignation as attorney general means that President Obama is losing one the longest-serving members of his cabinet and one of his closest confidants."
## [74] "The Department of Labor is putting money behind its push to expand paid family leave in the USA. The issue could be a factor in coming elections if enough voters ask about it."
## [75] "Members of the NewYork Police Department turned out in force on Thursday for the funeral of Officer Michael Williams in LaGrangeville, N.Y."
## [76] "How big a fight will President Obama put up over Attorney General Eric H. Holder Jr.s successor? The question is captivating Washington."
## [77] "Eric H. Holder Jr. has resigned as attorney general, but hes in no hurry to go anywhere."
## [78] "William H. Gross, who built Pimco into one of the largest asset managers in the world, will join Janus Capital after a decision had been made for him to leave Pimco or be forced out, said a person briefed on the matter."
## [79] "William H. Gross, for decades an investment guru, was undone by the increasing complexity of managing his ever-growing fund."
## [80] "Two of Allergans largest shareholders, T. Rowe Price and Pentwater Capital Management, have broken their silence to insist that the company not strike any deals before a scheduled special meeting in December."
## [81] "As Yahoo tries to figure out what to do after raising $6 billion from its stake in the Alibaba Group, a prominent investor, Starboard Value, has emerged to offer some suggestions including buying AOL."
## [82] "Speaker John A. Boehner says Republicans must compete in Northeast"
## [83] "The ruling by Judge Thomas P. Griesa of the Federal District Court in Manhattan allows Citigroup to make a $5 million payment to bondholders."
## [84] "Remembering James A. Trafficant, in videos and soundbites."
## [85] "The low-power computer server that has been seen as a way to revive Hewlett-Packard has never lived up to expectations. Still, HP keeps trying, this time with a chip made by ARM."
## [86] "William H. Gross abruptly leaves Pimco. | AIG case sheds new light on bailout. | DreamWorks Animation in sale talks with SoftBank. | Allergan shareholders speak out."
## [87] "Judge Thomas P. Griesa of Federal District Court in Manhattan stopped short of issuing sanctions, saying he would make a decision about them in the future."
## [88] "A suit filed by Stephen A. Wynn, the casino impresario, against an investor he accuses of slander is the latest salvo in the growing tensions between vocal shareholders and corporations."
## [89] "Kyle T. Dolan spins us a puzzle."
## [90] "The move, which the activist investor Carl C. Icahn had called for, will cleave eBay almost in half and separate it from a company that generates almost half its revenue."
## [91] "The team behind the popular Lower East Side restaurant are publishing a collection of 100 seasonal dishes meant to provide both information and inspiration. Here, they share a recipe with T."
## [92] "Elizabeth C. Gorski gives us the lowdown."
## [93] "The British media and marketing firms acquisition of Advanstar, a private company, would create the largest events organizer in the USA."
## [94] "The initial public offering of Pershing Square Holdings is expected to give the activist investor William A. Ackman a permanent pool of capital to make bigger, bolder bets."
## [95] "James R. Clapper Jr., the director of national intelligence, sent a message to employees on Tuesday defending the nations spy agencies against criticism."
## [96] "David A. ONeil has not announced his next step, though he will probably swing through Washingtons revolving door."
## [97] "A Hungarian musicologist finds Mozarts own score of the Piano Sonata in A, K. 331."
## [98] "After 20 years on the board of the Alvin Ailey Dance Foundation, Joan H. Weill, its chairwoman, is expected to announce Thursday that she is stepping down at the end of the year."
## [99] "Several recent bankruptcy cases and news events have shown that municipal and corporate debt securities have faced issues in pricing and trading, Stephen J. Lubben writes in an In Debt column."
## [100] "The countrys largest banks are rolling out a new service that could help them lessen their reliance on expensive data terminals like those sold by Bloomberg L.P."
## [101] "What White House security breaches happened during the presidencies of Franklin D. Roosevelt, Herbert Hoover and Ronald Reagan?"
## [102] "Politicians and NewYorks elite gathered Wednesday for the Alfred E. Smith charity dinner and roast."
## [103] "George W. Bush talks about his brother Jebs presidential aspirations."
## [104] "Im interested in buying a fairy house, I told the first branch of the phone tree at Bank of America Home Loans. In August, the company agreed to pay a $16.65 billion penalty for its role in selling bonds based on make-believe mortgages. I..."
## [105] "Michael R. Bloomberg and two Disney princesses."
## [106] "Former President George W. Bush said on Thursday that he had been lobbying his younger brother Jeb to throw his hat in the ring in 2016"
## [107] "The government said the criminal charges against Michael J. Coscia, founder of Panther Energy Trading, were the first to be brought under new rules that bar a type of abusive trading called spoofing."
## [108] "The Solomon R. Guggenheim Museum is planning the construction of a new building somewhere in NewYork City that will be used for offices, art storage and some public programming."
## [109] "Speaker John A. Boehner has picked Elise Stefanik, who is running for an open House seat in upstate NewYork, to deliver the partys radio address on Saturday."
## [110] "As the billionaire Kenneth C. Griffin battles his wife in divorce court, he has drawn back the curtain on one of the most prominent marriages in hedge fund world by disclosing terms of their prenuptial agreement."
## [111] "Jesse C. Litvak had been one of the few people convicted of fraud over the bailout of WallStreet, but an appeals court suggested that his conviction was likely to be overturned."
## [112] "Before my son started school, I shared my parents hippie view of the P.T.A. as a perfect-parent filled hassle and just another part of The Establishment trying to squelch creativity out of learning."
## [113] "Vice President Joseph R. Biden Jr. held a question and answer session at Harvard on Thursday."
## [114] "The investor Edward J. Bramson had been pushing to shake up the board of the British private equity firm Electra Private Equity and had sought two seats on its board."
## [115] "Including hard terms for AIG was politically necessary to getting the TARP program going, but Henry M. Paulson Jr. said he did support the bailout package and its terms."
## [116] "These D.I.Y. magazines are on the rise in LosAngeles, where artists are using them as a showcase for their work."
## [117] "A growing body of research indicates that many people who react to gluten may be suffering a condition called non-celiac gluten sensitivity, or NCGS."
## [118] "A big hurdle in the spoofing case against a high-frequency trading firm is that a jury must decide whether one computer fooling another is a crime, Peter J. Henning writes in the White Collar Watch column."
## [119] "Just 24 percent of respondents to a Pew Research Center poll correctly picked Janet L. Yellen as the Feds chairwoman from a list of four names."
## [120] "Maurice R. Greenberg, AIGs former chief and a large shareholder, has spun a ludicrous tale in court that the bailout of the insurer was unfair to its investors."
## [121] "Duncan L. Niederauer, who recently retired as head of the NewYork Stock Exchange, has joined an upstart brokerage firm called Battery East, which aims to help employees of privately held companies sell their shares."
## [122] "Big banks face another round of USA charges. | Henry M. Paulson Jr. testifies that punitive AIG terms were necessary. | Investors cheer the breakup of HP. | Lawyers for Goldman and Libyas sovereign fund clash in court."
## [123] "Timothy F. Geithner, the former Treasury secretary, was a witness in the trial of a lawsuit over the departments role in the bailout of AIG His book was under scrutiny, too."
## [124] "By chronicling the building blocks of technological advances, Walter Isaacsons The Innovators hopes to teach us about the nature of innovation, Jonathan A. Knee writes in a review, the debut Book Entry column."
## [125] "President Obama continues to get low marks on his handling of the threat from the Islamic State, also known as ISIS or ISIL."
## [126] "Valeant and Pershing Square are planning to raise their offer for Allergan. | Profit at Goldman less easy to find. | AIG trial puts Timothy F. Geithner on the hot seat. | Glencores chief pursues Rio Tinto."
## [127] "A derivatives rule is set to change. | JPMorgans data breach causes alarm. | Chinese companies are scooping up real estate across the globe. | Former Treasury Secretary Timothy F. Geithner defends the bailout of AIG"
## [128] "The $2.6 billion deal, which includes debt, is the latest transaction to reshape the drug industry. Auxilium is terminating its deal to acquire QLT."
## [129] "Carl C. Icahn, the billionaire activist investor, sent a letter to Timothy Cook, the chief executive of Apple, saying that the company is hugely undervalued."
## [130] "Critics want a war memorial to remove a sculpture based on Alfred Eisenstaedts famous Life Magazine photo of a sailor kissing a nurse at the end of World War II."
## [131] "One way to level the playing field is to simply repeal the safe harbors in the bankruptcy code, writes Stephen J. Lubben in the In Debt column."
## [132] "Facebook has a new local mobile advertising play. J.C. Penney is in the midst of an e-commerce renaissance."
## [133] "The AM1 Supreme by MARCH LA.B bears the hallmarks of vintage watches, but with a streamlined aesthetic."
## [134] "The government acknowledged there was scant legal precedent for its demand for $1.6 million in restitution from Jesse C. Litvak, who was convicted of securities fraud in March."
## [135] "Ben S. Bernanke, the former Federal Reserve chairman, kept his answers brief on the stand in the lawsuit over the 2008 bailout of American International Group."
## [136] "A sluggish global outlook sends ripples through the markets. | Tuning up UBSs investment bank. | Symantec announces a split. | Ben S. Bernanke defends the AIG bailout."
## [137] "The business will merge with a fledgling firm founded by Paul J. Taubman, the former Morgan Stanley investment banker, who will run the combined firm as chief executive and chairman."
## [138] "If you want to time-travel to the 1990s, youll get a chance at 1 p.m. today, when the William J. Clinton Library releases 10,000 more pages of previously undisclosed documents from the Clinton years."
## [139] "Field + Supply, founded by the interior designer Brad Ford, launches this weekend in a barn in High Falls, N.Y. Here, a look at some of the furniture and objects on offer."
## [140] "Ben Ratliff and Jon Caramanica discuss the singers Tinashe and FKA twigs, and whether they point toward a new conception of R&B."
## [141] "The Army War College rescinded the masters degree of Senator John E. Walsh on Friday, determining that the Montana Democrat plagiarized his final paper there in 2007."
## [142] "A restaurant learns what happens when you tell customers to pay what God wants them to pay, Microsofts chief executive says its O.K. for women to ask for a raise, and new restaurants are making Detroit a culinary oasis."
## [143] "A new USAO. lounge for service members and their families has opened at Terminal 5 at Kennedy Airport in NewYork."
## [144] "Vice President Spiro T. Agnew resigned on this day in 1973."
## [145] "Holly Fallon, whose stage name is Dollicious, and Miriam Hintz, who goes by Haru, posed in the lower courtyard of the Jacob K. Javits Convention Center on Friday at NewYork Comic Con."
## [146] "Two cases raise the question about what role the courts should play in policing negotiations and the limits that can be applied to the tactics one side can use in making a deal, Peter J. Henning writes in the White Collar Watch column."
## [147] "The awards will be announced on Nov. 23 in a show broadcast by ABC."
## [148] "A 28 year old digital communications director is helping to turn Speaker John A. Boehner, no ones idea of a digital-age guru, into a YouTube star."
## [149] "Abigail Johnson, 52, who is president of the parent company of Fidelity Investments, will succeed her father, Edward C. Johnson III, as chief executive."
## [150] "Photos from HongKong, Syria, Yemen and the USA."
## [151] "Adam G. Perl puns his way through our Tuesday puzzle."
## [152] "JPMorgan Chase posts a third quarter profit. | Warren E. Buffett promotes the Berkshire Hathaway brand. | Calculating the cost of Ebola | Derivatives change only goes so far."
## [153] "Denis J. McInerney, a former deputy assistant attorney general in the Justice Department, has returned to the law firm Davis Polk & Wardwell."
## [154] "President Obama has decided to wait until after next months midterm elections to nominate a replacement for Attorney General Eric H. Holder Jr., White House officials said."
## [155] "Some of it is OK."
## [156] "Highlights from the International Herald Tribune archives: Nikita S. Khrushchev was believed to be ousted as the Soviet leader in 1964."
## [157] "Hopper Drawing, by Carter E. Foster, was published by the Whitney Museum of American Art in connection with a Hopper exhibition in 2013."
## [158] "Airbnb, the pioneering home rental service, presents itself as useful and virtuous, but the reality is far less benign, according to Attorney General Eric T. Schneiderman of NewYork"
## [159] "The FBI director, James B. Comey, corrected statements he made on 60 Minutes that the bureau did not do electronic surveillance without a court order."
## [160] "Ben C. Solomon is a Times video journalist reporting on Ebola. His video today, about a team of ambulance drivers in Monrovia, Liberia, shows the dangers they face every day."
## [161] "James L. Amine and Timothy P. OHara have been appointed to the Swiss lenders executive board and will lead the investment banking division with Gal de Boissard, the chief executive for Europe, the Middle East and Africa."
## [162] "Food news from Silicon Valley to the UK."
## [163] "The venture capitalist Marc Andreessen is stepping down from eBays board, months after publicly defending his role as a director against attacks from Carl C. Icahn."
## [164] "Best known for his operas and symphonies, Mr. Glasss Piano tudes are the focus of a new recording and concerts at BAM."
## [165] "An SEC case against a high-frequency trading firm shows how difficult it is to draw the line between acceptable trading strategies and manipulation, Peter J. Henning writes in the White Collar Watch column."
## [166] "Vice President Joseph R. Biden Jr. appeared in a hangar near La Guardia Airport on Monday to endorse Gov. Andrew M. Cuomos plans for commissioning a redesign of four airports in and around NewYork City."
## [167] "Senator Patrick J. Leahy, chairman of the Judiciary Committee, is asking Comcast not to engage in paid prioritization of Internet content after its proposed takeover of Time Warner Cable."
## [168] "Tom Steyer, a billionaire hedge fund founder, has become the largest super PAC donor of all time, passing the casino magnate Sheldon G. Adelson."
## [169] "The piano superstar plays the solo cadenza for the first movement of Mozarts Piano Concerto No. 17 in G."
## [170] "If you are wondering why you have been getting fund-raising pitches from the Democratic Congressional Campaign Committee theres at least one explanation: an organization you did provide your email to has rented its list to the WashingtonDCC.C."
## [171] "Through a clever bit of photo manipulation, a new ad by the NRA puts Iowas Democratic Senate nominee, Bruce Braley, next to the man many gun owners consider their nemesis: Michael R. Bloomberg."
## [172] "Photos from SouthAfrica, HongKong, India and the USA."
## [173] "Vice President Joseph R. Biden Jr. beat the comics to the punch line at a recent Kennedy Center appearance."
## [174] "A federal judge ordered a full mental evaluation in the next 30 days of Omar J. Gonzalez, the man accused of jumping the fence and racing past Secret Service officers into the White House with a knife last month."
## [175] "Highlights from the International Herald Tribune archives: French farmers disagreed with General de Gaulles threat to quit the E.E.C. in 1964."
## [176] "The Feds policies that drove down interest rates to historically low levels have actually exacerbated the inequality problem that Janet L. Yellen said concerned her, writes William D. Cohan in Street Scene."
## [177] "Ben Bradlees funeral will be held Tuesday at the Washington National Cathedral, where Richard M. Nixon was remembered in a memorial service in 1994."
## [178] "NewYorkTimess David E. Sanger remembers a conversation with Ben Bradlee about an article in NewYorkTimes."
## [179] "At an investment conference, Scott A. Mather, who now oversees Pimcos Total Return Fund, defends its outsize holdings in European government bonds and other high-yielding securities."
## [180] "Children who drink rice, almond or soy milk instead of cows milk may have insufficient levels of vitamin D."
## [181] "A man later identified as a J.P. Morgan managing director walked into a live, online broadcast of a group of prominent protesters on Wednesday to vent his frustration with the continuing occupation of key locations in HongKong."
## [182] "Gov. Andrew M. Cuomo of NewYork foresees something really, really, really big for Hillary Rodham Clintons future."
## [183] "Richard Norton Smiths biography of Nelson Rockefeller is a portrait of the world when establishment financiers earned political respect, writes Jonathan A. Knee in a book review."
## [184] "A senior administration official confirms that Kathyrn Ruemmler, the former White House counsel, has taken herself out of the running to succeed Eric H. Holder Jr. as attorney general."
## [185] "The festival of theater, dance and performance art presented by P.S. 122 will take place at spaces around the city, while its home on First Avenue is renovated."
## [186] "Ben C. Solomon has been making videos about the Ebola outbreak, finding scenes of courage even as people struggle to contain the disease."
## [187] "Most European banks pass stress test. | Steven Ballmer could claim huge tax benefits on his LosAngeles Clippers deal. | William A. Ackmans outsize bets. | Court ruling disarms shareholders."
## [188] "Arguably the greatest feat of arms in American military history was performed on the Roanoke River at Plymouth, N.C., in the predawn darkness of Oct. 28, 1864."
## [189] "One side threatens to crack down harder; the other side complains about too much enforcement. The question is whether both sides can be satisfied, Peter J. Henning writes in the White Collar Watch column."
## [190] "Plus, El Anatsuis shimmering curtains, Martin Z. Marguliess photography collection and more art events in the week ahead."
## [191] "Vice President Joseph R. Biden campaigns for Representative Bruce Braley in Iowa on Monday."
## [192] "D.J. Tim Sweeneys cult-favorite program turns 15 next year. To celebrate, he has compiled a new double album, a song from which premieres here."
## [193] "William J. Burns, who just stepped down as deputy secretary of state, is being named president of the Carnegie Endowment for International Peace."
## [194] "Timothy D. Cook, Apples chief executive, said that one million credit cards had been activated on Apple Pay in the first three days that the mobile payment system was live."
## [195] "The information of more than 18.5 million California residents was compromised in 2013, according to Kamala D. Harris, Californias attorney general."
## [196] "An Apple executive recently called the design of Xiaomis phones theft. In return, Mr. Barra pointed out that parts of Apples new phones are a bit like handsets from HTC."
## [197] "Elizabeth C. Gorski cooks up a funny midweek puzzle for us."
## [198] "Michael E. Shapiro, who transformed and enlarged the collection of Atlantas High Museum, will leave next year."
## [199] "Sales of Gov. Andrew M. Cuomos memoir fell by more than 43 percent to 535 copies in its second week on shelves."
## [200] "Kering announced a new sustainable fashion program and the C.F.D.A. and Lexus announced the winners for their Eco-Fashion challenge."
## [201] "Quinn Bradlee, a son of Benjamin C. Bradlee, the former executive editor of The Washington Post, rested his head on the coffin after he spoke at his fathers funeral at Washington National Cathedral on Wednesday."
## [202] "He had claimed that the game publisher Activision Blizzard used his image without his permission in Call of Duty: Black Ops II."
## [203] "Senator Angus King, the Maine independent who had backed another independent, Eliot Cutler, for governor, switched his allegiance to the Democrat, Representative Michael H. Michaud."
## [204] "Photos from Turkey, China, India and the USA."
## [205] "Thomas M. Menino, who served as Bostons mayor for 20 years, has died at the age of 71."
## [206] "Neil L. Rudenstine has presided over the library along with Anthony W. Marx, its president during planning for the institutions controversial renovation."
## [207] "Tim Cooks public announcement that he is gay has been met with support, but some point out that the battle for L.G.B.T. equality at work is far from over."
## [208] "David W. Dunlap has worked at NewYorkTimes for 39 years. He recalls the early news coverage of AIDS and compares it to NewYorkTimess coverage of the arrival of the Ebola virus in the USA."
## [209] "Democratic nostalgia seemed to be the theme of the night as former President Bill Clinton campaigned on Thursday for Gov. Andrew M. Cuomo."
## [210] "The French banks results were a turnaround from the previous quarter, when it took a charge of nearly 6 billion euros for legal penalties in the USA."
## [211] "Albert G. Horvath, the Smithsonian Institutions current senior finance official, will serve as secretary until David J. Skorton can take up his permanent role in July."
## [212] "James B. Stewart provides insight into his column about the Apple chief executives announcement that he is proud to be gay."
## [213] "The return of Janet R. Bender."
## [214] "On Tuesday, Michael S. Dell will try to persuade people that his company is about far more than the personal computers and computer servers it has been known for, with products intended for things as varied as the cloud computing networks of global..."
## [215] "The government has imposed billions of dollars in penalties on the big banks for wrongdoing. But, Peter J. Henning writes in the White Collar Watch column, that may not be enough to change banks behavior."
## [216] "C.H.C.M., the influential Bond Street shop, is debuting an in-house range of refined wardrobe essentials."
## [217] "Photos from Turkey, Syria, India and the USA."
## [218] "Relations between tech companies and the intelligence community has been strained recently, but Adm. Michael S. Rogers played down government concerns."
## [219] "Highlights from the International Herald Tribune archives: Europeans were happy Lyndon B. Johnson was elected in 1964."
## [220] "Millstein & Company, the financial advisory firm founded by James E. Millstein, has hired Mark Walker, a former banker with Rothschild."
## [221] "Despite efforts to improve disclosure under the Freedom of Information Act, the SEC continues to resist sharing data with the public, William D. Cohan writes in the Street Scene column."
## [222] "William A. Ackmans hedge fund could vote its stake in Allergan in support of ousting directors at a meeting scheduled for Dec. 18, bolstering his effort to force the company into a sale."
## [223] "Some post-shellacking advice for the POTUS."
## [224] "Photos from Israel, Algeria, Russia and the USA."
## [225] "John L. Thornton, the chairman of Barrick Gold and a professor at Tsinghua University in Beijing, was designated as a possible successor to Henry M. Paulson Jr. when they were at Goldman Sachs."
## [226] "A. Jerrold Perenchio, the former chairman of Univision, pledged to donate about 50 artworks including notable Impressionist paintings "
## [227] "Rita Doves poem November for Beginners, and the article Heavy Autumn Snowstorm Barrels Across Northeast, by Al Baker, Elizabeth A. Harris and Sarah Maslin Nir, appear in this pairing."
## [228] "An emboldened Speaker John A. Boehner warned President Obama against unilateral action to overhaul the immigration system."
## [229] "The 82 year old has been recording with her daughter, Patsy L. Russell, and John Carter Cash, for a project combining new songs and old folk and gospel tunes."
## [230] "The designation of MetLife as systemically important is a sign that regulators are looking ahead to the next possible crisis, Stephen J. Lubben writes in the In Debt column."
## [231] "The Democratic Senatorial Campaign Committee cancels $2 million worth of ads on behalf of Senator Mary L. Landrieu of Louisiana."
## [232] "Photos from Ukraine, Syria, Scotland and the USA."
## [233] "Barry C. Silk writes puzzles for grown-ups, but once in a while you can find stuff for less mature adults like me."
## [234] "The Sony Pictures Classics co-president will take over from Herbert S. Schlosser, who will be chairman emeritus."
## [235] "Vice President Joseph R. Biden Jr. spoke quickly on his relationship with Prime Minister Benjamin Netanyahu of Israel during remarks to the Jewish Federation."
## [236] "Mr. Morrison will play J.M. Barrie in the show about the creation of Peter Pan; he and Jeremy Jordan have both played the role in earlier productions."
## [237] "The term political correctness is perhaps most often used by its detractors but new research has found that thinking about being P.C. can actually improve creativity."
## [238] "Senator Patrick J. Leahy said he believes Loretta E. Lynch, President Obamas choice for attorney general, should be confirmed easily no matter when the Senate votes."
## [239] "By analyzing samples from each of the five NewYork City boroughs, Jane M. Carlton hopes to detect viruses before an outbreak."
## [240] "Is the food label \"natural\" misleading to consumers? Should the F.D.A. enforce a definition of the term?"
## [241] "Another small sampling of sparkling prose in recent editions, from serious to silly to Kenny G."
## [242] "President Obama and President Vladimir V. Putin of Russia discussed Iran, Syria and Ukraine during three brief conversations at the Asia-Pacific Economic Cooperation summit meeting."
## [243] "Andrea C. Bonomi, the Italian businessman battling a Chinese French bid for Club Mditerrane, announces a sweetened offer for the resort company."
## [244] "Reporters were surprised on Tuesday morning when the Federalist Society, a conservative legal group, announced that the news media would be barred from an appearance by Justice Samuel A. Alito Jr. at its annual gala black-tie dinner."
## [245] "Heaven, the first offering from the Birmingham raised, LosAngeles-based 24 year old T.O.L.D., premieres here."
## [246] "The activist investor William A. Ackman has acquired an 8.5 percent economic interest in the former animal health arm of Pfizer."
## [247] "Photos from France, China, Poland and the USA."
## [248] "Both steps fall short of the big breakup of Dow Chemical being advocated by the activist hedge fund manager Daniel S. Loeb."
## [249] "Mr. Grammer is to play the largely comic role of Charles Frohman, the theater producer of Peter Pan playwright J.M. Barrie."
## [250] "Photos from Turkey, HongKong, Afghanistan and the USA."
## [251] "Steven A. Ballmer, Microsofts former chief executive, is making a financial contribution that will enable the school to expand its computer science faculty by about 50 percent."
## [252] "Dassault Systmes, a French company, has a five year research agreement with the F.D.A. focused on using 3 D technology for simulating the reliability of pacemaker wires."
## [253] "Its in the bullet points: Shoes, bags, more mobile and social media, and a focus on the USA."
## [254] "Berkshire Hathaway, the conglomerate run by Warren E. Buffett, will acquire Duracell using a transaction aimed at lowering the tax bill."
## [255] "President Obama and Prime Minister Dmitri A. Medvedev of Russia at the East Asia summit plenary session in Naypyidaw, Myanmar, on Thursday."
## [256] "Readers wrote about a public reckoning for our wars in Iraq and Afghanistan and the sacrifices of our troops in comments on Daniel P. Bolgers Op-Ed, The Truth About the Wars."
## [257] "Warren E. Buffett has an appetite for large and creatively structured acquisitions. A look back at some of his recent deals."
## [258] "Advanced Placement courses can be a real boon to students motivated by intellectual curiosity and a love of learning. But for students looking to please their parents or for those in pursuit of transcript padding and other false academic idols, AP..."
## [259] "Thomas O. Kriegsmann is to be its director of programs, a new position that will include artist development projects."
## [260] "David W. Dunlap, a Metro reporter for NewYorkTimes, took us back to the moment when the paper stopped being produced with Linotype machines."
## [261] "Daniel S. Loeb, the activist hedge fund manager, has been pressing for a breakup of Dow Chemical and has been rebuffed in his attempt to name two directors."
## [262] "More than $40 million has been raised in honor of Joan H. Weill, who is stepping down as chairwoman of the Alvin Ailey Dance Foundation."
## [263] "Net neutrality could give Comcast a way out of its Time Warner Cable acquisition. | Warren E. Buffetts deal for Duracell satisfies a number of his deal-making preferences. | Virgin America to begin trading. | Goldman seeks to lure tech talent."
## [264] "Evan R. Chesler, the chairman of the law firm Cravath, Swaine & Moore, will take over as the library chairman from Neil L. Rudenstine."
## [265] "Finish Big and Succession, examine how entrepreneurs and corporate chieftains alike can be assessed on their departures, Jonathan A. Knee writes in a joint book review."
## [266] "Senator Mary L. Landrieus campaign has not hesitated to embrace her stance on the XL Pipeline ahead of her runoff election against Representative Bill Cassidy."
## [267] "Celebrities should not quit their day jobs, according to Andrew J. Ries."
## [268] "And what about that I.S.I. dreamboat Aasar Khan?"
## [269] "The alliance between the American and German companies is expected to strengthen Mercks oncology business in several important markets, including the USA."
## [270] "The animal health care company Zoetis, which has attracted the interest of the deal maker William A. Ackman, has made a deal of its own."
## [271] "The $66 billion acquisition by Actavis would be the largest deal this year and would thwart the takeover effort of Valeant and William A. Ackman."
## [272] "Representative Nancy Pelosi has a birthday wish for Speaker John A. Boehner."
## [273] "Insider trading cases are casting a spotlight on whether courts should defer to administrative agencies in determining criminal violations, Peter J. Henning writes in the White Collar Watch column."
## [274] "Representative Steve Israel joked about handing off the WashingtonDCC.C. chairmanship."
## [275] "Mayor Francis G. Slay said troops operating within the city of SaintLouis would have a secondary role in responding to any protests, and that police officers would be the ones dealing directly with demonstrators."
## [276] "What do you do when you are a billionaire and a movie buff? If you are Paul G. Allen, you turn the iconic theater you already own into a state-of-the-art venue."
## [277] "William A. Ackman and Valeant Pharmaceuticals lost their hostile battle to buy Allergan and still came away with a total of $2.6 billion."
## [278] "WallStreet is on pace for a big year of deal-making. | William A. Ackman may have lost Allergan, but he still came away a winner. | The Justice Department is weighing a civil suit against Angelo R. Mozilo, the former chief executive of Countrywide..."
## [279] "A briefing book for the White House chief of staff, Denis R. McDonough, had a note from one of his children attached to it as he sat in a meeting on Ebola in the White House on Tuesday."
## [280] "Much like their leader, Warren Buffett, the chief executives of Berkshire Hathaways subsidiaries are a savvy group of acquirers. Lawrence A. Cunningham, in the Another View column, explains why."
## [281] "American Ballet Theater and the Segerstrom Center for the Arts in Costa Mesa, Calif., will open the American Ballet Theater William J. Gillespie School on the Segerstrom campus in September, both organizations announced on Tuesday."
## [282] "Assembling the tale of G.M.s faulty ignition switch, which led to the deaths of at least 13 people, took months of digging by a team of reporters and editors."
## [283] "Joseph P. Clancy, acting director of the Secret Service, is to testify before Congress."
## [284] "Thomas B. Leonardi will be joining the investment bank as a senior adviser, focusing on the insurance industry."
## [285] "The release of an interview Robert Rubin gave nine years ago offers a window into the thoughts of the rich and powerful, William D. Cohan writes in the Street Scene column."
## [286] "On Saturday, the Arcangel-composed series Dances for the Electric Piano will be staged for the first time in the USA. Listen to it here."
## [287] "Representative Paul D. Ryan, the new chairman of the Ways and Means Committee, said that a broad overhaul of the tax code depended on the administrations engagement."
## [288] "Gov. Bobby Jindal of Louisiana tried to cut off an immigration discussion with Chuck Todd of NBC."
## [289] "The country plans to scrap its state monopoly on the sale of salt, a move that would mark the end of a system that can be traced back to 685 B.C."
## [290] "Despite being about to receive his Ph.D. in physics from Harvard, Kevin Niu plans to eschew the academic life to follow his calling in the entertainment industry."
## [291] "Daniel K. Tarullo, the Fed governor, described to a Senate subcommittee potential rules that could restrict banks from some types of commodities operations."
## [292] "Speaker John A. Boehner addressed President Obamas immigration actions."
## [293] "Is the Federal Reserve up to the task of regulating financial institutions that are so large and complex? the chairman of a Senate panel asked William C. Dudley."
## [294] "Attorney General Eric H. Holder Jr. offered guidance to law enforcement agencies and urged protesters to be restrained."
## [295] "Vice President Joseph R. Biden Jr. had to beat a hasty retreat on Friday at the sight of an unruly crowd of Ukrainians in Kiev protesting the new government amid continuing unrest in that former Soviet republic."
## [296] "Paul J. Taubman is bringing Don Cornwell, a managing director who specializes in sports team deals, to his boutique investment bank."
## [297] "At noon on Friday a handful of people performed a mock lynching across the street from the Old Courthouse in SaintLouis as a silent protest of the police killings of Michael Brown and Vonderrit D. Myers Jr."
## [298] "Last week was a rough one for the Federal Reserve. | Paul J. Taubman continues to poach talent from Morgan Stanley, his former employer. | Mathew Martoma has started his nine year prison term for insider trading."
## [299] "The Chapter 11 filing by Aereo prompts a larger question of whether bankruptcy courts should actively police debtors motives for filing cases, writes Stephen J. Lubben in the In Debt column."
## [300] "The Fed has to balance the conflicting roles it plays as an overseer of the banks and as a law enforcement agency pursuing misconduct, Peter J. Henning writes in the White Collar Watch column."
## [301] "Speaker John A. Boehner announced that he was reappointing Representative Trey Gowdy, Republican of SouthCarolina, as chairman of the special committee on Benghazi for the 114th Congress that begins in January."
## [302] "Senator Elizabeth Warrens wrath toward Antonio F. Weiss is misdirected, and her understanding of the inversion deal on which she bases her opposition appears misinformed."
## [303] "Senator Charles E. Schumer said Tuesday that it was a political mistake to pass the Affordable Care Act in 2010 because voters at the time were looking for relief from the recession not universal health care."
## [304] "Photos from Egypt, HongKong, Kenya and the USA."
## [305] "The return of Michael S. Maurer."
## [306] "Thanksgiving was my Korean familys annual recommitment ceremony to the USA."
## [307] "The potential political costs of the civil unrest in Ferguson, Mo.; speculation about Chuck Hagels replacement; and a look at Speaker John A. Boehners turkey brine recipe."
## [308] "StevenACohens ex-wife gets outside financing for her lawsuit. | Poison puts develop a downside. | Trustee moves to repay Mt. Gox creditors. | Philip A. Falcone to Depart the Harbinger Group."
## [309] "Photos from HongKong, China, Syria and the USA."
## [310] "In Newport, R.I., visitors can tour storied mansions decorated for Christmas."
## [311] "Michael B. Jordan and John Turturro will take part in a reading of the script for Spike Lees Do the Right Thing"
## [312] "David W. Dunlap, a Metro reporter and the writer of the Building Blocks column, explored the sometimes close connection between Mr. Castro and NewYorkTimes."
## [313] "A wide open grid from Elizabeth C. Gorski."
## [314] "Celebrate the holidays with Elizabeth C. Gorski."
## [315] "Programs, and payouts, to encourage whistle-blowers to come forward with wrongdoing are likely to increase with the new Congress, Peter J. Henning writes."
## [316] "Jeh C. Johnson, the Homeland Security secretary, is prepared to defend the presidents decision to take executive action to overhaul the immigration system."
## [317] "Photos from HongKong, Paris, SouthKorea and the USA."
## [318] "The reporters J. David Goodman and Michael Wilson described a suburban mothers descent to a heroin addict whose home on Staten Island served as a drug-dealing center."
## [319] "Chief Justice John G. Roberts Jr. is well known for his ability to quote obscure case law, but on Monday he invoked the rapper Eminem at the Supreme Court."
## [320] "Mary L. Landrieu, a three term senator, is in the race of her career against the Republican Bill Cassidy as Louisiana voters head to the polls for a runoff vote on Dec. 6."
## [321] "Bank of NewYork Mellon said on Tuesday that it would give a board seat to Edward P. Garden of the hedge fund Trian Fund Management, forestalling a potentially costly proxy fight."
## [322] "The deal for IndCor Properties will make Singapores sovereign wealth fund a major owner of warehouses and distribution centers in the USA."
## [323] "Speaker John A. Boehner on options that Republicans are considering to block President Obamas immigration action."
## [324] "Senator Joseph R. McCarthy was censured 60 years ago today."
## [325] "Jonah M. Kessel filmed a documentary video in China and Myanmar, where jade mines have become a free market for heroin, and tells of the story of an addict he encountered."
## [326] "In a spellbinding letter to Jim Cramer, the activist investor J. Carlo Cannell urged him to cut his pay 70 percent, resign from CNBC and direct his energy to helping your fellow shareholders crawl back from Hades."
## [327] "The Fox affiliate in NewOrleans is taking issue with the use of its news anchors words in a campaign ad for Senator Mary L. Landrieu."
## [328] "NewYorks chokehold case has thrown a volatile element into Loretta E. Lynchs confirmation hearings as USA attorney general."
## [329] "Senator Elizabeth Warrens opposition to a nominee for a role at the Treasury Department demonstrates her blatant political motivation, William D. Cohan writes in the Street Scene column."
## [330] "J. Carlo Cannell wants Jim Cramer to sell TheStreet or take a pay cut and quit CNBC. A sale may be wishful thinking, says Quentin Webb of Reuters Breakingviews. But Mr. Cramers payout is hard to justify."
## [331] "The appellate division of the NewYork State Supreme Court dismissed a lawsuit at the heart of a bitter dispute between the billionaire collector Ronald O. Perelman and the art dealer Larry Gagosian."
## [332] "Photos from Russia, SouthAfrica, India and the USA."
## [333] "Andrea C. Bonomi, the Italian businessman fighting a Chinese investor for Club Mditerrane, raised his bid to $29.65 a share for the French resort operator."
## [334] "President Obama on Friday announced his selection of Ashton B. Carter to lead the Pentagon, White House officials said, embracing a physicist and national security centrist who may advocate a stronger use of American power."
## [335] "Jon Caramanica and A.O. Scott discuss what this update of The Bodyguard gets right about the music business."
## [336] "The famously outspoken, leather-clad architect is being honored this week with Design Miamis inaugural Design Visionary Award and an exhibition at the Bass Museum of Art. In this video, he discusses his career with T."
## [337] "An architect who hopes to save a Paul Rudolph-designed building in Orange County, N.Y., presented his detailed proposal to county leaders there on Friday."
## [338] "Demonstrations focused on fatal police encounters continued on Saturday, with a rally at the Louis H. Pink Houses in Brooklyn to protest the fatal shooting of Akai Gurley there and a grand jurys decision not to indict an officer in the death of..."
## [339] "Will the SECs treatment of Bank of America, Peter J. Henning asks in the White Collar Watch column, become the norm for other banks seeking waivers from rules on bad actors?"
## [340] "After years of research, there is genuine hope for reducing the toll exacted by lung cancer, which is the leading cause of cancer deaths in the USA."
## [341] "Senator Charles E. Schumer of NewYork may be Republicans favorite Democrat these days."
## [342] "Recommendations from the American Bankruptcy Institute failed to address derivatives held by nonfinancial institutions that will keep operating, writes Stephen J. Lubben in the In Debt column."
## [343] "Readers expressed outrage and despair in comments on a Friday editorial, It Wasnt Just the Chokehold: Eric Garner, Daniel Pantaleo and Lethal Police Tactics, and on an Op-Ed from Eric L. Adams, We Must Stop Abuse of Black Men."
## [344] "Jessica Bibliowicz will become chairwoman of the Cornell Weill Medical College, effective Jan. 1. Her father, Sanford I. Weill, has been chairman for two decades."
## [345] "The SecondMarket exchange won all but one of the 20 Bitcoin blocks in the governments auction. The venture capitalist Timothy C. Draper, who swept the first auction, won only 2,000 Bitcoins this time."
## [346] "Brian T. Moynihan tells a conference that his bank is expecting trading revenue in the fourth quarter to be down from last quarter and from a year earlier."
## [347] "The revival of A.R. Gurneys play will close this Sunday, before some of its scheduled stars ever get to perform."
## [348] "Jonathan Gruber, the M.IT economist who advised the Obama administration on the Affordable Care Act, apologized on Tuesday for inflammatory comments that have brought negative attention to the law in recent months."
## [349] "The Senate Intelligence Committee has released its long-awaited review on the torture of prisoners held by the Central Intelligence Agency during the George W. Bush administration."
## [350] "Just hours after announcing its $8.4 billion acquisition of Cubist Pharmaceuticals, a Delaware judge invalidated patents owned by the antibiotics maker. Merck appears to have been caught up in the M.&.A. exuberance, says Robert Cyran of Reuters..."
## [351] "Recent criticism of James J. Cramers compensation from TheStreet.com has painted him as another greedy WallStreet executive looking out for himself but not shareholders."
## [352] "Gov. Chris Christie of NewJersey, who was appointed USA attorney for the state by President George W. Bush in the weeks after the Sept. 11 attacks, seemed not eager on Wednesday to discuss the Senate report on the brutal American..."
## [353] "In this lesson we offer three different teaching ideas to engage students in the debate over immigration policy in the USA."
## [354] "The latest appeals court decision will make some insider-trading cases harder, but it does not give a free pass to hedge funds to trade on any confidential information, Peter J. Henning writes in White Collar Watch."
## [355] "The latest NewYorkTimes poll is based on telephone interviews conducted Dec. 4 through 7 with 1,006 adults throughout the USA."
## [356] "The Confucius Peace Prize was first given out in late 2010 as a rejoinder to the Nobel Peace Prize. President Vladimir V. Putin of Russia is a past recipient."
## [357] "Were going to continue to press his nomination forward, Treasury Secretary Jacob J. Lew said. He also spoke about corporate taxes and sanctions on Russia."
## [358] "George R. Goldner will become a private art adviser after 21 years heading the department. Nadine M. Orensteinhas been named to succeed him."
## [359] "Lloyd C. Blankfein, Goldman Sachss chief executive, addressed the antagonism that has been coming out of Washington."
## [360] "Francis J. Shammo, Verizons chief financial officer, said the company planned to continue to invest in its FiOS fiber-optic network and its wireless systems regardless of the outcome of the broadband debate."
## [361] "Senator Dianne Feinstein, chairwoman of the committee that investigated the CIAs interrogation program, offered a live rebuttal as John O. Brennan, the agencys director, responded to the committees findings."
## [362] "President Obama and Vice President Joseph R. Biden Jr. both made calls to Democratic lawmakers on Thursday in an urgent effort to save a $1.1 trillion spending bill that the White House and Republicans say is a worthy compromise."
## [363] "The Blackstone Group, the private equity giant run by Stephen A. Schwarzman, is almost finished raising a new fund for energy investments that is expected to exceed $4 billion in assets."
## [364] "OscarSSchafer will succeed the current NewYork Philharmonic chairman, Gary W. Parr, early next year."
## [365] "Friday and the weekend: a struggle for L.G.B.T. protections, more chilly weather, protests on Saturday, and the week in pictures."
## [366] "An A.C.L.U. video features military personnel who objected to the Bush-era torture program."
## [367] "Two new books provide insight into lesser-known stages of Charles M. Schulz and Jack Kirbys careers."
## [368] "On this day in 2000, the Supreme Court ruled 5 to 4 that there would be no additional counting of presidential votes in Florida. 35 days after Election Day, the decision effectively handed the presidency to George W. Bush."
## [369] "Hours after casting his final vote as a member of Congress, Representative John D. Dingell was hospitalized after taking a spill earlier in the week."
## [370] "Jungle Lady is the first single off the NewYork-based act Lion Babes self-titled debut EP."
## [371] "The Securities and Exchange Commission said it would not be able to proceed with the lawsuit because the two main witnesses it intended to call to testify are in Poland and had no plans to return to the USA."
## [372] "The return of Joel D. Lafargue, after a long break."
## [373] "Nominees that didnt make the final cut this year include Nine Inch Nails, Sting, N.W.A, Chic and Kraftwerk."
## [374] "The disturbing statistics on military rape and the fleeting attention of the public led Mary F. Calvert to document the stories of those who survived assaults."
## [375] "James E. Staley, a managing partner at the NewYork hedge fund BlueMountain Capital Management, is expected to be considered for the board during UBSs annual meeting in May, and to join the banks risk committee."
## [376] "Wednesday: The M.T.A. honors its workers, oddly warm weather, and a vigil for Pakistan."
## [377] "President Obama is scheduled to speak about relations with Cuba at 12 p.m. ET."
## [378] "Highlights and video of the remarks by President Raul Castro of Cuba on a diplomatic breakthrough with the USA."
## [379] "Speaker John A. Boehner on Wednesday called President Obamas new policy on Cuba the latest in a long line of mindless concessions to a dictatorship that brutalizes its people and schemed with our enemies."
## [380] "Alan P. Gross prepared his remarks at his attorneys office in Washington on Wednesday."
## [381] "Most Americans support normalizing trade and diplomatic relations with Cuba and consider what happens in the communist nation to be important to the interests of the USA."
## [382] "Photos from Pakistan, Australia, France and the USA."
## [383] "President Obama took the occasion of Alan P. Grosss release not only to acknowledge the interest the Jewish community had taken in Mr. Grosss case, but also to connect his release to the holiday and to Jewish teaching."
## [384] "Curtis L. Buser, who has been interim finance chief for the last seven months, is taking over the job permanently."
## [385] "Speaker John A. Boehner posted a holiday greeting on Thursday, a video poem title Happy Christmas to All, although the Speakers wishes may not extend all the way to the White House."
## [386] "Recommended books for learning more about Cubas history and its relationship to the USA."
## [387] "In a speech on Thursday, Benjamin M. Lawsky, NewYork States top financial regulator, provided the most detail yet on revisions to his proposed Bitcoin rules."
## [388] "The firm promoted John E. Waldron, one of its star deal makers, to become a co-head of its investment bank, succeeding a scion of one of Goldmans most famous families."
## [389] "The disposal is the latest move for Xerox, which has been transformed under the leadership of Ursula M. Burns into a provider of various business services."
## [390] "Speaker John A. Boehner has invited President Obama to address the country on Jan. 20 and give a State of the Union speech before a joint session of the new Republican-controlled Congress."
## [391] "Mark A. Flaherty and Mark O. Winkelman will serve on a board that now has 14 members."
## [392] "Goldman names John E. Waldron co-head of its investment bank. | Regulators deem MetLife too big to fail. | Another big whistle-blower reward in Bank of America case. | London tenants win battle over USA equity firm."
## [393] "After an investigation into conflicts of interest, William C. Erbey has agreed to step down from his position as chairman of Ocwen and four other related companies."
## [394] "Its about non-digital IT."
## [395] "Fred R. Conrad may be best known for his exquisite portraits, but an assignment in Kosovo taught him the value of watching and waiting for the story to come to him."
## [396] "An overhaul of the Freedom of Information Act did not survive the legislative deal-making that produced the spending plan. William D. Cohan laments that loss in the Street Scene column."
## [397] "Its hard to be FDR."
## [398] "Ocwen should look at Clayton Homes, whose credo puts customers first, writes Lawrence A. Cunningham in Another View."
## [399] "Photos from Ukraine, India, Nepal and the USA."
## [400] "NewYorkTimes reporter David W. Dunlap explains how coverage of the Bay of Pigs disaster unfolded."
## [401] "After decades of oppression of people who identify as L.G.B.T., the Cuban government now pays for gender reassignment surgery."
## [402] "Look for the hidden message from Peter A. Collins."
## [403] "The new year will bring cases from the subprime mortgage crisis, a Justice Department decision on an appeal of an insider trading ruling and, perhaps, new accounting frauds, Peter J. Henning writes in the White Collar Watch column."
## [404] "Under the F.D.A.s new policy, any man who has had sex with another man in the last year may not donate."
## [405] "Representative Michael G. Grimm of NewYork insisted last week that he would not resign from his seat. On Monday night, he changed his mind."
## [406] "Speaker John A. Boehner gave his seal of approval to Representative Michael Grimms resignation."
## [407] "David E. Sanger, chief Washington correspondent for NewYorkTimes, recalled a visit to the country 25 years ago."
## [408] "Former President George H. W. Bush was released from a Houston hospital where he has been staying since last week after experiencing shortness of breath."
## [409] "Speaker John A. Boehner expressed his support for Representative Steve Scalise, who spoke at a conference for white supremacists in 2002."
## [1] "Remaining Acronyms in Abstract:"
## [1] "In the 1864 election, Gotham delivered nearly twice as many votes to the presidents opponent, George B. McClellan."
## [2] "Researchers are finding more evidence that women who take S.S.R.I. depressants like Prozac and Zoloft increase the likelihood of a variety of health problems in their newborns."
## [3] "Eric T. Schneiderman, NewYorks attorney general, filed a suit on Tuesday that accuses Evans Bank of denying mortgages to African-Americans in Buffalo regardless of their credit."
## [4] "Jeffrey H. Knox, a senior federal prosecutor who butted heads with a number of WallStreet banks, is switching sides."
## [5] "In which Peter A. Collins disproves Aesop."
## [6] "Antonia M. Apps, the lead federal prosecutor in the criminal trial last year of Michael Steinberg, is taking a job with the law firm Milbank, Tweed, Hadley & McCloy."
## [7] "Scholarly musicians prove that Ph.D.s can play."
## [8] "In this new series, Chris Labzda and Bon Duke, the co-founders of the NewYork Fashion Film Festival, curate a short film each week for T. This weeks installment: A collage of classics overlaid with sound from Lauren Wolksteins Social Butterfly."
## [9] "I moved to Hudson, N.Y., to slow down and save money. I didnt know Id feel so lonely."
## [10] "A study suggests that exercise can help kids, especially those with A.D.H.D., focus in class."
## [11] "How should the USA respond to the killings of Steven J. Sotloff and James Foley?"
## [12] "Although banks are hiring armies of legal and compliance professionals in response to settlements and fines, they would do better to find ways to streamline their existing legal work, Geoffrey A. Moore and Mark Harris write in an Another View column."
## [13] "William T. Shermans 1864 campaign to take the Georgia city was one of the bloodiest of the Civil War."
## [14] "George Martin, author of the Song of Ice and Fire series, will be promoting his new book at the 92nd St. Y."
## [15] "The workers are OK."
## [16] "The Securities and Exchange Commission has chosen Tracey L. McNeil, an agency veteran and former corporate lawyer, to act as a liaison in resolving problems that retail investors may have with the agency."
## [17] "John A. Paulsons hedge fund suffered across-the-board losses in July and did only marginally better in August, according to an update to investors."
## [18] "Mr. Sheen will direct and perform in Dylan Thomass work he called it a play for voices on Oct. 26 at the 92nd Street Y."
## [19] "The acquisition is the latest move by Jeffrey R. Immelt, the chief of GE, to refocus the conglomerate on its core industrial businesses."
## [20] "The Chinese state news media has cited concerns that the USA is indoctrinating Chinese students by including its founding documents upholding freedom and human rights in the SAT."
## [21] "At the midpoint of the range, the Citizens Financial Group, based in Providence, R.I., would be valued at $13.4 billion."
## [22] "Mark P. Frissoras decision comes a few weeks after Carl C. Icahn disclosed an 8.48 percent stake in the rental car provider."
## [23] "Martin Lipton is virtually guaranteeing the continuation of policies opposed by many NYU faculty members, William D. Cohan writes in the Street Scene column."
## [24] "China Central Television used images of Condoleezza Rice in a report about a Beijing visit by Susan E. Rice, President Obamas national security adviser."
## [25] "If youve never heard the name Lloyd J. Austin, theres a good reason"
## [26] "This weeks video features Ismenia Mendes and David McElwee in a scene from A. R. Gurneys 1977 drama The Wayside Motor Inn."
## [27] "The media and financial data company Thomson Reuters is looking to sell peHUB, Buyouts and Venture Capital Journal, according to a report by peHUB."
## [28] "News headlines briefly flashed that Carl C. Icahn had raised his stake in Gannett, the media company, to nearly 9 percent. But as it turns out, his firm simply made a typo in a regulatory filing."
## [29] "Just two days after her USA Open victory, Serena Williams hosted her first ever fashion show for HSN."
## [30] "Nearly 70 years after they parted, a Chinese veteran is on a quest to find a Japanese woman he met shortly after World War II. His tale offers a warmer recollection of that era."
## [31] "In this lesson we offer a series of topics and questions paired with Times essays, articles, slide shows and videos to help students dig deeper into the causes, effects and overall legacy of World War I."
## [32] "A collection of material related to Rosa Parks, bought last month by Howard G. Buffett, is being lent to the Library of Congress for 10 years."
## [33] "A soldier takes time out of uniform to pursue an M.F.A. in writing, and discovers that what sets him apart, as a veteran at the keyboard, is not as important as the common ground he shares with other writers."
## [34] "Worn by the chef from July 29 to Aug. 4 while cooking at his two restaurants, Blue Hill in Manhattan and Blue Hill at Stone Barns in Pocantico Hills, N.Y."
## [35] "Gov. Andrew M. Cuomo and Mayor Bill de Blasio both enjoyed an Italian sausage at the Feast of SanGennaro in Little Italy on Saturday."
## [36] "Eike Batista was accused of manipulating the share price of his now-bankrupt petroleum company OGX."
## [37] "The activist investor William A. Ackmans new fund, Pershing Square Holdings, aims to list on the the Euronext Amsterdam exchange on Oct. 13."
## [38] "Fraud in the market for penny stocks continues unabated, two criminal cases filed last week show, Peter J. Henning writes in the White Collar Watch column."
## [39] "Quarries and connecting trenches were nothing less than small cities, where the walls bear witness to the people who fought and died in World War I."
## [40] "The deal for Vkontakte ends tensions between the social networks minority shareholders and Alisher B. Usmanov, the Russian billionaire who controls Mail.ru."
## [41] "Sears is borrowing $400 million from its chief, the billionaire Edward S. Lampert, through his hedge fund."
## [42] "KKRs purchase of Pioneers D.J. audio equipment business is the modern-day equivalent of providing gold-rush pickaxes to participants in the rave rush, Jeffrey Goldfarb writes in Reuters Breakingviews."
## [43] "Two grandchildren of Dwight D. Eisenhower say proposed changes by Frank Gehry to his memorial to the former president do not satisfy their concerns."
## [44] "In a letter to Electra Private Equity shareholders, the activist investor Edward J. Bramson said a change in approach in Electras strategy could increase the value of its shares by about $1.6 billion."
## [45] "Prosecutors in Brazil have argued that Mr. Batista profited from insider information when he sold shares in his now bankrupt petroleum company, OGX."
## [46] "Comments by Marshall L. Miller, the No. 2 official in the Justice Departments criminal division, reflect the agencys renewed interest in charging individual bank employees rather than just the banks."
## [47] "The top job at the database giant will be shared by Mark V. Hurd, now co-president, and Safra Catz, who is co-president and chief financial officer."
## [48] "Alibaba is set to start trading on the NewYork Stock Exchange under the ticker symbol BABA. | Markets showed relief that the United Kingdom would not face a tumultuous breakup. | Lawrence J. Ellison announced his retirement as chief executive of Oracle."
## [49] "Average waist circumference but not B.M.I. has increased significantly in the USA, a new study reports."
## [50] "The British private equity firm accused Edward J. Bramsons Sherborne Investors Management of making unverifiable statements and unsubstantiated claims in its effort to reshape Electra."
## [51] "Alibaba soared in its public market debut. | Siemens agreed to buy the Dresser-Rand Group. | EMC weighed a deal with H.P. | Public pension funds may be souring on hedge funds."
## [52] "Venezuela may represent the Latin American and Caribbean nations and sit right next to the USA."
## [53] "Speaker John A. Boehner seemed to blame lazy Americans for the stalled economy."
## [54] "It was unclear whether President Petro O. Poroshenko would travel to NewYork to deliver his address as scheduled on Thursday."
## [55] "As the reclusive genius Richard D. James releases his first album in 13 years, the graphic designer with whom he has frequently collaborated discusses their work together."
## [56] "Vice President Joseph R. Biden Jr. met Carina Castro, the daughter of Julin Castro, secretary of Housing and Urban Development, at a reception honoring Hispanic Heritage Month."
## [57] "Attorney General Eric H. Holder Jr. will announce Tuesday that the federal government will end its fiscal year next week with 4,800 fewer prisoners, the first time since 1980 that the inmate population has declined from year to year."
## [58] "President Obama could end up leaving a smaller imprint on the judiciary than either President Bill Clinton or President George W. Bush."
## [59] "Ever wonder why jewelry stores hide the price tags? How invoice factoring works for small businesses. And why German companies are on a buying spree in the USA."
## [60] "Speaker John A. Boehner urges the new Veterans Affairs secretary to look to the private sector in reviewing how the department delivers health care to veterans."
## [61] "On this day in 1952, Richard M. Nixon gave his famous Checkers speech."
## [62] "To kick off Paris Fashion Week, the chic native whose shirting-based collection is stocked at Barneys NewYork and Dover Street Market shares a list of her favorite places around town with T."
## [63] "Gov. Paul R. LePage of Maine wants to help the NFL address its domestic violence problem."
## [64] "Maurice R. Greenberg, the former chief of AIG, has now raised several million dollars from three WallStreet investors to help cover the cost of the case."
## [65] "Electra said that two influential shareholder advisory services opposed efforts of the activist investor Edward J. Bramson, through his Sherborne Investors Management, to shake up the board of the British private equity firm."
## [66] "Representative James A. Traficant was seriously injured after a tractor accident on his family farm Tuesday night, according to local media reports in Youngstown, Ohio."
## [67] "The role of former President Gerald R. Ford in the filing of a friend-of-the-court brief in a Michigan affirmative action case."
## [68] "The insider trading case against Michael A. Lucarelli, a former executive at Lippert/Heilshorn and Associates, was relatively minor. But he got attention for running barefoot from the court in August."
## [69] "Judge Richard M. Berman of Federal District Court in Manhattan, before sentencing conservative author Dinesh DSouza to probation for violating federal campaign finance laws."
## [70] "Speaker John A. Boehner, an avid golfer, is gearing up for the Ryder Cup, the intense competition between the USA and Europe that begins Friday in Scotland."
## [71] "Speaker John A. Boehner tells First Draft that the new Congress should debate military action in Syria."
## [72] "A new poll has Senator Mark R. Warner, the Democratic incumbent in Virginia, up by 9 points over his Republican opponent."
## [73] "Eric H. Holder Jr.s resignation as attorney general means that President Obama is losing one the longest-serving members of his cabinet and one of his closest confidants."
## [74] "The Department of Labor is putting money behind its push to expand paid family leave in the USA. The issue could be a factor in coming elections if enough voters ask about it."
## [75] "Members of the NewYork Police Department turned out in force on Thursday for the funeral of Officer Michael Williams in LaGrangeville, N.Y."
## [76] "How big a fight will President Obama put up over Attorney General Eric H. Holder Jr.s successor? The question is captivating Washington."
## [77] "Eric H. Holder Jr. has resigned as attorney general, but hes in no hurry to go anywhere."
## [78] "William H. Gross, who built Pimco into one of the largest asset managers in the world, will join Janus Capital after a decision had been made for him to leave Pimco or be forced out, said a person briefed on the matter."
## [79] "William H. Gross, for decades an investment guru, was undone by the increasing complexity of managing his ever-growing fund."
## [80] "Two of Allergans largest shareholders, T. Rowe Price and Pentwater Capital Management, have broken their silence to insist that the company not strike any deals before a scheduled special meeting in December."
## [81] "As Yahoo tries to figure out what to do after raising $6 billion from its stake in the Alibaba Group, a prominent investor, Starboard Value, has emerged to offer some suggestions including buying AOL."
## [82] "Speaker John A. Boehner says Republicans must compete in Northeast"
## [83] "The ruling by Judge Thomas P. Griesa of the Federal District Court in Manhattan allows Citigroup to make a $5 million payment to bondholders."
## [84] "Remembering James A. Trafficant, in videos and soundbites."
## [85] "The low-power computer server that has been seen as a way to revive Hewlett-Packard has never lived up to expectations. Still, HP keeps trying, this time with a chip made by ARM."
## [86] "William H. Gross abruptly leaves Pimco. | AIG case sheds new light on bailout. | DreamWorks Animation in sale talks with SoftBank. | Allergan shareholders speak out."
## [87] "Judge Thomas P. Griesa of Federal District Court in Manhattan stopped short of issuing sanctions, saying he would make a decision about them in the future."
## [88] "A suit filed by Stephen A. Wynn, the casino impresario, against an investor he accuses of slander is the latest salvo in the growing tensions between vocal shareholders and corporations."
## [89] "Kyle T. Dolan spins us a puzzle."
## [90] "The move, which the activist investor Carl C. Icahn had called for, will cleave eBay almost in half and separate it from a company that generates almost half its revenue."
## [91] "The team behind the popular Lower East Side restaurant are publishing a collection of 100 seasonal dishes meant to provide both information and inspiration. Here, they share a recipe with T."
## [92] "Elizabeth C. Gorski gives us the lowdown."
## [93] "The British media and marketing firms acquisition of Advanstar, a private company, would create the largest events organizer in the USA."
## [94] "The initial public offering of Pershing Square Holdings is expected to give the activist investor William A. Ackman a permanent pool of capital to make bigger, bolder bets."
## [95] "James R. Clapper Jr., the director of national intelligence, sent a message to employees on Tuesday defending the nations spy agencies against criticism."
## [96] "David A. ONeil has not announced his next step, though he will probably swing through Washingtons revolving door."
## [97] "A Hungarian musicologist finds Mozarts own score of the Piano Sonata in A, K. 331."
## [98] "After 20 years on the board of the Alvin Ailey Dance Foundation, Joan H. Weill, its chairwoman, is expected to announce Thursday that she is stepping down at the end of the year."
## [99] "Several recent bankruptcy cases and news events have shown that municipal and corporate debt securities have faced issues in pricing and trading, Stephen J. Lubben writes in an In Debt column."
## [100] "The countrys largest banks are rolling out a new service that could help them lessen their reliance on expensive data terminals like those sold by Bloomberg L.P."
## [101] "What White House security breaches happened during the presidencies of Franklin D. Roosevelt, Herbert Hoover and Ronald Reagan?"
## [102] "Politicians and NewYorks elite gathered Wednesday for the Alfred E. Smith charity dinner and roast."
## [103] "George W. Bush talks about his brother Jebs presidential aspirations."
## [104] "Michael R. Bloomberg and two Disney princesses."
## [105] "Former President George W. Bush said on Thursday that he had been lobbying his younger brother Jeb to throw his hat in the ring in 2016"
## [106] "The government said the criminal charges against Michael J. Coscia, founder of Panther Energy Trading, were the first to be brought under new rules that bar a type of abusive trading called spoofing."
## [107] "The Solomon R. Guggenheim Museum is planning the construction of a new building somewhere in NewYork City that will be used for offices, art storage and some public programming."
## [108] "Speaker John A. Boehner has picked Elise Stefanik, who is running for an open House seat in upstate NewYork, to deliver the partys radio address on Saturday."
## [109] "As the billionaire Kenneth C. Griffin battles his wife in divorce court, he has drawn back the curtain on one of the most prominent marriages in hedge fund world by disclosing terms of their prenuptial agreement."
## [110] "Jesse C. Litvak had been one of the few people convicted of fraud over the bailout of WallStreet, but an appeals court suggested that his conviction was likely to be overturned."
## [111] "Before my son started school, I shared my parents hippie view of the P.T.A. as a perfect-parent filled hassle and just another part of The Establishment trying to squelch creativity out of learning."
## [112] "Vice President Joseph R. Biden Jr. held a question and answer session at Harvard on Thursday."
## [113] "The investor Edward J. Bramson had been pushing to shake up the board of the British private equity firm Electra Private Equity and had sought two seats on its board."
## [114] "Including hard terms for AIG was politically necessary to getting the TARP program going, but Henry M. Paulson Jr. said he did support the bailout package and its terms."
## [115] "These D.I.Y. magazines are on the rise in LosAngeles, where artists are using them as a showcase for their work."
## [116] "A growing body of research indicates that many people who react to gluten may be suffering a condition called non-celiac gluten sensitivity, or NCGS."
## [117] "A big hurdle in the spoofing case against a high-frequency trading firm is that a jury must decide whether one computer fooling another is a crime, Peter J. Henning writes in the White Collar Watch column."
## [118] "Just 24 percent of respondents to a Pew Research Center poll correctly picked Janet L. Yellen as the Feds chairwoman from a list of four names."
## [119] "Maurice R. Greenberg, AIGs former chief and a large shareholder, has spun a ludicrous tale in court that the bailout of the insurer was unfair to its investors."
## [120] "Duncan L. Niederauer, who recently retired as head of the NewYork Stock Exchange, has joined an upstart brokerage firm called Battery East, which aims to help employees of privately held companies sell their shares."
## [121] "Big banks face another round of USA charges. | Henry M. Paulson Jr. testifies that punitive AIG terms were necessary. | Investors cheer the breakup of HP. | Lawyers for Goldman and Libyas sovereign fund clash in court."
## [122] "Timothy F. Geithner, the former Treasury secretary, was a witness in the trial of a lawsuit over the departments role in the bailout of AIG His book was under scrutiny, too."
## [123] "By chronicling the building blocks of technological advances, Walter Isaacsons The Innovators hopes to teach us about the nature of innovation, Jonathan A. Knee writes in a review, the debut Book Entry column."
## [124] "President Obama continues to get low marks on his handling of the threat from the Islamic State, also known as ISIS or ISIL."
## [125] "Valeant and Pershing Square are planning to raise their offer for Allergan. | Profit at Goldman less easy to find. | AIG trial puts Timothy F. Geithner on the hot seat. | Glencores chief pursues Rio Tinto."
## [126] "A derivatives rule is set to change. | JPMorgans data breach causes alarm. | Chinese companies are scooping up real estate across the globe. | Former Treasury Secretary Timothy F. Geithner defends the bailout of AIG"
## [127] "The $2.6 billion deal, which includes debt, is the latest transaction to reshape the drug industry. Auxilium is terminating its deal to acquire QLT."
## [128] "Carl C. Icahn, the billionaire activist investor, sent a letter to Timothy Cook, the chief executive of Apple, saying that the company is hugely undervalued."
## [129] "Critics want a war memorial to remove a sculpture based on Alfred Eisenstaedts famous Life Magazine photo of a sailor kissing a nurse at the end of World War II."
## [130] "One way to level the playing field is to simply repeal the safe harbors in the bankruptcy code, writes Stephen J. Lubben in the In Debt column."
## [131] "Facebook has a new local mobile advertising play. J.C. Penney is in the midst of an e-commerce renaissance."
## [132] "The AM1 Supreme by MARCH LA.B bears the hallmarks of vintage watches, but with a streamlined aesthetic."
## [133] "The government acknowledged there was scant legal precedent for its demand for $1.6 million in restitution from Jesse C. Litvak, who was convicted of securities fraud in March."
## [134] "Ben S. Bernanke, the former Federal Reserve chairman, kept his answers brief on the stand in the lawsuit over the 2008 bailout of American International Group."
## [135] "A sluggish global outlook sends ripples through the markets. | Tuning up UBSs investment bank. | Symantec announces a split. | Ben S. Bernanke defends the AIG bailout."
## [136] "The business will merge with a fledgling firm founded by Paul J. Taubman, the former Morgan Stanley investment banker, who will run the combined firm as chief executive and chairman."
## [137] "If you want to time-travel to the 1990s, youll get a chance at 1 p.m. today, when the William J. Clinton Library releases 10,000 more pages of previously undisclosed documents from the Clinton years."
## [138] "Field + Supply, founded by the interior designer Brad Ford, launches this weekend in a barn in High Falls, N.Y. Here, a look at some of the furniture and objects on offer."
## [139] "Ben Ratliff and Jon Caramanica discuss the singers Tinashe and FKA twigs, and whether they point toward a new conception of R&B."
## [140] "The Army War College rescinded the masters degree of Senator John E. Walsh on Friday, determining that the Montana Democrat plagiarized his final paper there in 2007."
## [141] "A restaurant learns what happens when you tell customers to pay what God wants them to pay, Microsofts chief executive says its O.K. for women to ask for a raise, and new restaurants are making Detroit a culinary oasis."
## [142] "A new USAO. lounge for service members and their families has opened at Terminal 5 at Kennedy Airport in NewYork."
## [143] "Vice President Spiro T. Agnew resigned on this day in 1973."
## [144] "Holly Fallon, whose stage name is Dollicious, and Miriam Hintz, who goes by Haru, posed in the lower courtyard of the Jacob K. Javits Convention Center on Friday at NewYork Comic Con."
## [145] "Two cases raise the question about what role the courts should play in policing negotiations and the limits that can be applied to the tactics one side can use in making a deal, Peter J. Henning writes in the White Collar Watch column."
## [146] "The awards will be announced on Nov. 23 in a show broadcast by ABC."
## [147] "A 28 year old digital communications director is helping to turn Speaker John A. Boehner, no ones idea of a digital-age guru, into a YouTube star."
## [148] "Abigail Johnson, 52, who is president of the parent company of Fidelity Investments, will succeed her father, Edward C. Johnson III, as chief executive."
## [149] "Photos from HongKong, Syria, Yemen and the USA."
## [150] "Adam G. Perl puns his way through our Tuesday puzzle."
## [151] "JPMorgan Chase posts a third quarter profit. | Warren E. Buffett promotes the Berkshire Hathaway brand. | Calculating the cost of Ebola | Derivatives change only goes so far."
## [152] "Denis J. McInerney, a former deputy assistant attorney general in the Justice Department, has returned to the law firm Davis Polk & Wardwell."
## [153] "President Obama has decided to wait until after next months midterm elections to nominate a replacement for Attorney General Eric H. Holder Jr., White House officials said."
## [154] "Some of it is OK."
## [155] "Highlights from the International Herald Tribune archives: Nikita S. Khrushchev was believed to be ousted as the Soviet leader in 1964."
## [156] "Hopper Drawing, by Carter E. Foster, was published by the Whitney Museum of American Art in connection with a Hopper exhibition in 2013."
## [157] "Airbnb, the pioneering home rental service, presents itself as useful and virtuous, but the reality is far less benign, according to Attorney General Eric T. Schneiderman of NewYork"
## [158] "The FBI director, James B. Comey, corrected statements he made on 60 Minutes that the bureau did not do electronic surveillance without a court order."
## [159] "Ben C. Solomon is a Times video journalist reporting on Ebola. His video today, about a team of ambulance drivers in Monrovia, Liberia, shows the dangers they face every day."
## [160] "James L. Amine and Timothy P. OHara have been appointed to the Swiss lenders executive board and will lead the investment banking division with Gal de Boissard, the chief executive for Europe, the Middle East and Africa."
## [161] "Food news from Silicon Valley to the UK."
## [162] "The venture capitalist Marc Andreessen is stepping down from eBays board, months after publicly defending his role as a director against attacks from Carl C. Icahn."
## [163] "Best known for his operas and symphonies, Mr. Glasss Piano tudes are the focus of a new recording and concerts at BAM."
## [164] "An SEC case against a high-frequency trading firm shows how difficult it is to draw the line between acceptable trading strategies and manipulation, Peter J. Henning writes in the White Collar Watch column."
## [165] "Vice President Joseph R. Biden Jr. appeared in a hangar near La Guardia Airport on Monday to endorse Gov. Andrew M. Cuomos plans for commissioning a redesign of four airports in and around NewYork City."
## [166] "Senator Patrick J. Leahy, chairman of the Judiciary Committee, is asking Comcast not to engage in paid prioritization of Internet content after its proposed takeover of Time Warner Cable."
## [167] "Tom Steyer, a billionaire hedge fund founder, has become the largest super PAC donor of all time, passing the casino magnate Sheldon G. Adelson."
## [168] "The piano superstar plays the solo cadenza for the first movement of Mozarts Piano Concerto No. 17 in G."
## [169] "If you are wondering why you have been getting fund-raising pitches from the Democratic Congressional Campaign Committee theres at least one explanation: an organization you did provide your email to has rented its list to the WashingtonDCC.C."
## [170] "Through a clever bit of photo manipulation, a new ad by the NRA puts Iowas Democratic Senate nominee, Bruce Braley, next to the man many gun owners consider their nemesis: Michael R. Bloomberg."
## [171] "Photos from SouthAfrica, HongKong, India and the USA."
## [172] "Vice President Joseph R. Biden Jr. beat the comics to the punch line at a recent Kennedy Center appearance."
## [173] "A federal judge ordered a full mental evaluation in the next 30 days of Omar J. Gonzalez, the man accused of jumping the fence and racing past Secret Service officers into the White House with a knife last month."
## [174] "Highlights from the International Herald Tribune archives: French farmers disagreed with General de Gaulles threat to quit the E.E.C. in 1964."
## [175] "The Feds policies that drove down interest rates to historically low levels have actually exacerbated the inequality problem that Janet L. Yellen said concerned her, writes William D. Cohan in Street Scene."
## [176] "Ben Bradlees funeral will be held Tuesday at the Washington National Cathedral, where Richard M. Nixon was remembered in a memorial service in 1994."
## [177] "NewYorkTimess David E. Sanger remembers a conversation with Ben Bradlee about an article in NewYorkTimes."
## [178] "At an investment conference, Scott A. Mather, who now oversees Pimcos Total Return Fund, defends its outsize holdings in European government bonds and other high-yielding securities."
## [179] "Children who drink rice, almond or soy milk instead of cows milk may have insufficient levels of vitamin D."
## [180] "A man later identified as a J.P. Morgan managing director walked into a live, online broadcast of a group of prominent protesters on Wednesday to vent his frustration with the continuing occupation of key locations in HongKong."
## [181] "Gov. Andrew M. Cuomo of NewYork foresees something really, really, really big for Hillary Rodham Clintons future."
## [182] "A hedge fund has emerged as perhaps the last hope for two struggling retails. | Bankers are jockeying for the next sovereign debt deal in Africa, despite plenty of risks. | Shareholders decide today whether to approve Chiquitas plan to acquire an Irish rival. | Charles T. Munger gives to theoretical physics."
## [183] "Richard Norton Smiths biography of Nelson Rockefeller is a portrait of the world when establishment financiers earned political respect, writes Jonathan A. Knee in a book review."
## [184] "A senior administration official confirms that Kathyrn Ruemmler, the former White House counsel, has taken herself out of the running to succeed Eric H. Holder Jr. as attorney general."
## [185] "The festival of theater, dance and performance art presented by P.S. 122 will take place at spaces around the city, while its home on First Avenue is renovated."
## [186] "Ben C. Solomon has been making videos about the Ebola outbreak, finding scenes of courage even as people struggle to contain the disease."
## [187] "Most European banks pass stress test. | Steven Ballmer could claim huge tax benefits on his LosAngeles Clippers deal. | William A. Ackmans outsize bets. | Court ruling disarms shareholders."
## [188] "Arguably the greatest feat of arms in American military history was performed on the Roanoke River at Plymouth, N.C., in the predawn darkness of Oct. 28, 1864."
## [189] "One side threatens to crack down harder; the other side complains about too much enforcement. The question is whether both sides can be satisfied, Peter J. Henning writes in the White Collar Watch column."
## [190] "Plus, El Anatsuis shimmering curtains, Martin Z. Marguliess photography collection and more art events in the week ahead."
## [191] "Vice President Joseph R. Biden campaigns for Representative Bruce Braley in Iowa on Monday."
## [192] "D.J. Tim Sweeneys cult-favorite program turns 15 next year. To celebrate, he has compiled a new double album, a song from which premieres here."
## [193] "William J. Burns, who just stepped down as deputy secretary of state, is being named president of the Carnegie Endowment for International Peace."
## [194] "Timothy D. Cook, Apples chief executive, said that one million credit cards had been activated on Apple Pay in the first three days that the mobile payment system was live."
## [195] "The information of more than 18.5 million California residents was compromised in 2013, according to Kamala D. Harris, Californias attorney general."
## [196] "An Apple executive recently called the design of Xiaomis phones theft. In return, Mr. Barra pointed out that parts of Apples new phones are a bit like handsets from HTC."
## [197] "Elizabeth C. Gorski cooks up a funny midweek puzzle for us."
## [198] "Michael E. Shapiro, who transformed and enlarged the collection of Atlantas High Museum, will leave next year."
## [199] "Sales of Gov. Andrew M. Cuomos memoir fell by more than 43 percent to 535 copies in its second week on shelves."
## [200] "Kering announced a new sustainable fashion program and the C.F.D.A. and Lexus announced the winners for their Eco-Fashion challenge."
## [201] "Quinn Bradlee, a son of Benjamin C. Bradlee, the former executive editor of The Washington Post, rested his head on the coffin after he spoke at his fathers funeral at Washington National Cathedral on Wednesday."
## [202] "He had claimed that the game publisher Activision Blizzard used his image without his permission in Call of Duty: Black Ops II."
## [203] "Senator Angus King, the Maine independent who had backed another independent, Eliot Cutler, for governor, switched his allegiance to the Democrat, Representative Michael H. Michaud."
## [204] "Photos from Turkey, China, India and the USA."
## [205] "Thomas M. Menino, who served as Bostons mayor for 20 years, has died at the age of 71."
## [206] "Neil L. Rudenstine has presided over the library along with Anthony W. Marx, its president during planning for the institutions controversial renovation."
## [207] "Tim Cooks public announcement that he is gay has been met with support, but some point out that the battle for L.G.B.T. equality at work is far from over."
## [208] "David W. Dunlap has worked at NewYorkTimes for 39 years. He recalls the early news coverage of AIDS and compares it to NewYorkTimess coverage of the arrival of the Ebola virus in the USA."
## [209] "Democratic nostalgia seemed to be the theme of the night as former President Bill Clinton campaigned on Thursday for Gov. Andrew M. Cuomo."
## [210] "The French banks results were a turnaround from the previous quarter, when it took a charge of nearly 6 billion euros for legal penalties in the USA."
## [211] "Albert G. Horvath, the Smithsonian Institutions current senior finance official, will serve as secretary until David J. Skorton can take up his permanent role in July."
## [212] "James B. Stewart provides insight into his column about the Apple chief executives announcement that he is proud to be gay."
## [213] "The return of Janet R. Bender."
## [214] "On Tuesday, Michael S. Dell will try to persuade people that his company is about far more than the personal computers and computer servers it has been known for, with products intended for things as varied as the cloud computing networks of global enterprises and handy personal devices."
## [215] "The government has imposed billions of dollars in penalties on the big banks for wrongdoing. But, Peter J. Henning writes in the White Collar Watch column, that may not be enough to change banks behavior."
## [216] "C.H.C.M., the influential Bond Street shop, is debuting an in-house range of refined wardrobe essentials."
## [217] "Photos from Turkey, Syria, India and the USA."
## [218] "Relations between tech companies and the intelligence community has been strained recently, but Adm. Michael S. Rogers played down government concerns."
## [219] "Highlights from the International Herald Tribune archives: Europeans were happy Lyndon B. Johnson was elected in 1964."
## [220] "Millstein & Company, the financial advisory firm founded by James E. Millstein, has hired Mark Walker, a former banker with Rothschild."
## [221] "Despite efforts to improve disclosure under the Freedom of Information Act, the SEC continues to resist sharing data with the public, William D. Cohan writes in the Street Scene column."
## [222] "William A. Ackmans hedge fund could vote its stake in Allergan in support of ousting directors at a meeting scheduled for Dec. 18, bolstering his effort to force the company into a sale."
## [223] "Some post-shellacking advice for the POTUS."
## [224] "Photos from Israel, Algeria, Russia and the USA."
## [225] "John L. Thornton, the chairman of Barrick Gold and a professor at Tsinghua University in Beijing, was designated as a possible successor to Henry M. Paulson Jr. when they were at Goldman Sachs."
## [226] "A. Jerrold Perenchio, the former chairman of Univision, pledged to donate about 50 artworks including notable Impressionist paintings "
## [227] "Rita Doves poem November for Beginners, and the article Heavy Autumn Snowstorm Barrels Across Northeast, by Al Baker, Elizabeth A. Harris and Sarah Maslin Nir, appear in this pairing."
## [228] "An emboldened Speaker John A. Boehner warned President Obama against unilateral action to overhaul the immigration system."
## [229] "The 82 year old has been recording with her daughter, Patsy L. Russell, and John Carter Cash, for a project combining new songs and old folk and gospel tunes."
## [230] "The designation of MetLife as systemically important is a sign that regulators are looking ahead to the next possible crisis, Stephen J. Lubben writes in the In Debt column."
## [231] "The Democratic Senatorial Campaign Committee cancels $2 million worth of ads on behalf of Senator Mary L. Landrieu of Louisiana."
## [232] "Photos from Ukraine, Syria, Scotland and the USA."
## [233] "Barry C. Silk writes puzzles for grown-ups, but once in a while you can find stuff for less mature adults like me."
## [234] "The Sony Pictures Classics co-president will take over from Herbert S. Schlosser, who will be chairman emeritus."
## [235] "Vice President Joseph R. Biden Jr. spoke quickly on his relationship with Prime Minister Benjamin Netanyahu of Israel during remarks to the Jewish Federation."
## [236] "Mr. Morrison will play J.M. Barrie in the show about the creation of Peter Pan; he and Jeremy Jordan have both played the role in earlier productions."
## [237] "The term political correctness is perhaps most often used by its detractors but new research has found that thinking about being P.C. can actually improve creativity."
## [238] "Senator Patrick J. Leahy said he believes Loretta E. Lynch, President Obamas choice for attorney general, should be confirmed easily no matter when the Senate votes."
## [239] "By analyzing samples from each of the five NewYork City boroughs, Jane M. Carlton hopes to detect viruses before an outbreak."
## [240] "Is the food label \"natural\" misleading to consumers? Should the F.D.A. enforce a definition of the term?"
## [241] "Another small sampling of sparkling prose in recent editions, from serious to silly to Kenny G."
## [242] "President Obama and President Vladimir V. Putin of Russia discussed Iran, Syria and Ukraine during three brief conversations at the Asia-Pacific Economic Cooperation summit meeting."
## [243] "Andrea C. Bonomi, the Italian businessman battling a Chinese French bid for Club Mditerrane, announces a sweetened offer for the resort company."
## [244] "Reporters were surprised on Tuesday morning when the Federalist Society, a conservative legal group, announced that the news media would be barred from an appearance by Justice Samuel A. Alito Jr. at its annual gala black-tie dinner."
## [245] "Heaven, the first offering from the Birmingham raised, LosAngeles-based 24 year old T.O.L.D., premieres here."
## [246] "The activist investor William A. Ackman has acquired an 8.5 percent economic interest in the former animal health arm of Pfizer."
## [247] "Photos from France, China, Poland and the USA."
## [248] "Both steps fall short of the big breakup of Dow Chemical being advocated by the activist hedge fund manager Daniel S. Loeb."
## [249] "Mr. Grammer is to play the largely comic role of Charles Frohman, the theater producer of Peter Pan playwright J.M. Barrie."
## [250] "Photos from Turkey, HongKong, Afghanistan and the USA."
## [251] "Steven A. Ballmer, Microsofts former chief executive, is making a financial contribution that will enable the school to expand its computer science faculty by about 50 percent."
## [252] "Dassault Systmes, a French company, has a five year research agreement with the F.D.A. focused on using 3 D technology for simulating the reliability of pacemaker wires."
## [253] "Its in the bullet points: Shoes, bags, more mobile and social media, and a focus on the USA."
## [254] "Berkshire Hathaway, the conglomerate run by Warren E. Buffett, will acquire Duracell using a transaction aimed at lowering the tax bill."
## [255] "President Obama and Prime Minister Dmitri A. Medvedev of Russia at the East Asia summit plenary session in Naypyidaw, Myanmar, on Thursday."
## [256] "Readers wrote about a public reckoning for our wars in Iraq and Afghanistan and the sacrifices of our troops in comments on Daniel P. Bolgers Op-Ed, The Truth About the Wars."
## [257] "Warren E. Buffett has an appetite for large and creatively structured acquisitions. A look back at some of his recent deals."
## [258] "Thomas O. Kriegsmann is to be its director of programs, a new position that will include artist development projects."
## [259] "David W. Dunlap, a Metro reporter for NewYorkTimes, took us back to the moment when the paper stopped being produced with Linotype machines."
## [260] "Daniel S. Loeb, the activist hedge fund manager, has been pressing for a breakup of Dow Chemical and has been rebuffed in his attempt to name two directors."
## [261] "More than $40 million has been raised in honor of Joan H. Weill, who is stepping down as chairwoman of the Alvin Ailey Dance Foundation."
## [262] "Net neutrality could give Comcast a way out of its Time Warner Cable acquisition. | Warren E. Buffetts deal for Duracell satisfies a number of his deal-making preferences. | Virgin America to begin trading. | Goldman seeks to lure tech talent."
## [263] "Evan R. Chesler, the chairman of the law firm Cravath, Swaine & Moore, will take over as the library chairman from Neil L. Rudenstine."
## [264] "Finish Big and Succession, examine how entrepreneurs and corporate chieftains alike can be assessed on their departures, Jonathan A. Knee writes in a joint book review."
## [265] "Senator Mary L. Landrieus campaign has not hesitated to embrace her stance on the XL Pipeline ahead of her runoff election against Representative Bill Cassidy."
## [266] "Celebrities should not quit their day jobs, according to Andrew J. Ries."
## [267] "And what about that I.S.I. dreamboat Aasar Khan?"
## [268] "The alliance between the American and German companies is expected to strengthen Mercks oncology business in several important markets, including the USA."
## [269] "The animal health care company Zoetis, which has attracted the interest of the deal maker William A. Ackman, has made a deal of its own."
## [270] "The $66 billion acquisition by Actavis would be the largest deal this year and would thwart the takeover effort of Valeant and William A. Ackman."
## [271] "Representative Nancy Pelosi has a birthday wish for Speaker John A. Boehner."
## [272] "Insider trading cases are casting a spotlight on whether courts should defer to administrative agencies in determining criminal violations, Peter J. Henning writes in the White Collar Watch column."
## [273] "Representative Steve Israel joked about handing off the WashingtonDCC.C. chairmanship."
## [274] "Mayor Francis G. Slay said troops operating within the city of SaintLouis would have a secondary role in responding to any protests, and that police officers would be the ones dealing directly with demonstrators."
## [275] "What do you do when you are a billionaire and a movie buff? If you are Paul G. Allen, you turn the iconic theater you already own into a state-of-the-art venue."
## [276] "William A. Ackman and Valeant Pharmaceuticals lost their hostile battle to buy Allergan and still came away with a total of $2.6 billion."
## [277] "WallStreet is on pace for a big year of deal-making. | William A. Ackman may have lost Allergan, but he still came away a winner. | The Justice Department is weighing a civil suit against Angelo R. Mozilo, the former chief executive of Countrywide Financial. | USA plans second Bitcoin auction."
## [278] "A briefing book for the White House chief of staff, Denis R. McDonough, had a note from one of his children attached to it as he sat in a meeting on Ebola in the White House on Tuesday."
## [279] "Much like their leader, Warren Buffett, the chief executives of Berkshire Hathaways subsidiaries are a savvy group of acquirers. Lawrence A. Cunningham, in the Another View column, explains why."
## [280] "American Ballet Theater and the Segerstrom Center for the Arts in Costa Mesa, Calif., will open the American Ballet Theater William J. Gillespie School on the Segerstrom campus in September, both organizations announced on Tuesday."
## [281] "Assembling the tale of G.M.s faulty ignition switch, which led to the deaths of at least 13 people, took months of digging by a team of reporters and editors."
## [282] "Joseph P. Clancy, acting director of the Secret Service, is to testify before Congress."
## [283] "Thomas B. Leonardi will be joining the investment bank as a senior adviser, focusing on the insurance industry."
## [284] "The release of an interview Robert Rubin gave nine years ago offers a window into the thoughts of the rich and powerful, William D. Cohan writes in the Street Scene column."
## [285] "On Saturday, the Arcangel-composed series Dances for the Electric Piano will be staged for the first time in the USA. Listen to it here."
## [286] "Representative Paul D. Ryan, the new chairman of the Ways and Means Committee, said that a broad overhaul of the tax code depended on the administrations engagement."
## [287] "Gov. Bobby Jindal of Louisiana tried to cut off an immigration discussion with Chuck Todd of NBC."
## [288] "The country plans to scrap its state monopoly on the sale of salt, a move that would mark the end of a system that can be traced back to 685 B.C."
## [289] "Despite being about to receive his Ph.D. in physics from Harvard, Kevin Niu plans to eschew the academic life to follow his calling in the entertainment industry."
## [290] "Daniel K. Tarullo, the Fed governor, described to a Senate subcommittee potential rules that could restrict banks from some types of commodities operations."
## [291] "Speaker John A. Boehner addressed President Obamas immigration actions."
## [292] "Is the Federal Reserve up to the task of regulating financial institutions that are so large and complex? the chairman of a Senate panel asked William C. Dudley."
## [293] "Attorney General Eric H. Holder Jr. offered guidance to law enforcement agencies and urged protesters to be restrained."
## [294] "Vice President Joseph R. Biden Jr. had to beat a hasty retreat on Friday at the sight of an unruly crowd of Ukrainians in Kiev protesting the new government amid continuing unrest in that former Soviet republic."
## [295] "Paul J. Taubman is bringing Don Cornwell, a managing director who specializes in sports team deals, to his boutique investment bank."
## [296] "At noon on Friday a handful of people performed a mock lynching across the street from the Old Courthouse in SaintLouis as a silent protest of the police killings of Michael Brown and Vonderrit D. Myers Jr."
## [297] "Last week was a rough one for the Federal Reserve. | Paul J. Taubman continues to poach talent from Morgan Stanley, his former employer. | Mathew Martoma has started his nine year prison term for insider trading."
## [298] "The Chapter 11 filing by Aereo prompts a larger question of whether bankruptcy courts should actively police debtors motives for filing cases, writes Stephen J. Lubben in the In Debt column."
## [299] "The Fed has to balance the conflicting roles it plays as an overseer of the banks and as a law enforcement agency pursuing misconduct, Peter J. Henning writes in the White Collar Watch column."
## [300] "Speaker John A. Boehner announced that he was reappointing Representative Trey Gowdy, Republican of SouthCarolina, as chairman of the special committee on Benghazi for the 114th Congress that begins in January."
## [301] "Senator Elizabeth Warrens wrath toward Antonio F. Weiss is misdirected, and her understanding of the inversion deal on which she bases her opposition appears misinformed."
## [302] "Senator Charles E. Schumer said Tuesday that it was a political mistake to pass the Affordable Care Act in 2010 because voters at the time were looking for relief from the recession not universal health care."
## [303] "Photos from Egypt, HongKong, Kenya and the USA."
## [304] "The return of Michael S. Maurer."
## [305] "Thanksgiving was my Korean familys annual recommitment ceremony to the USA."
## [306] "The potential political costs of the civil unrest in Ferguson, Mo.; speculation about Chuck Hagels replacement; and a look at Speaker John A. Boehners turkey brine recipe."
## [307] "StevenACohens ex-wife gets outside financing for her lawsuit. | Poison puts develop a downside. | Trustee moves to repay Mt. Gox creditors. | Philip A. Falcone to Depart the Harbinger Group."
## [308] "Photos from HongKong, China, Syria and the USA."
## [309] "In Newport, R.I., visitors can tour storied mansions decorated for Christmas."
## [310] "Michael B. Jordan and John Turturro will take part in a reading of the script for Spike Lees Do the Right Thing"
## [311] "David W. Dunlap, a Metro reporter and the writer of the Building Blocks column, explored the sometimes close connection between Mr. Castro and NewYorkTimes."
## [312] "A wide open grid from Elizabeth C. Gorski."
## [313] "Celebrate the holidays with Elizabeth C. Gorski."
## [314] "Programs, and payouts, to encourage whistle-blowers to come forward with wrongdoing are likely to increase with the new Congress, Peter J. Henning writes."
## [315] "Jeh C. Johnson, the Homeland Security secretary, is prepared to defend the presidents decision to take executive action to overhaul the immigration system."
## [316] "Photos from HongKong, Paris, SouthKorea and the USA."
## [317] "The reporters J. David Goodman and Michael Wilson described a suburban mothers descent to a heroin addict whose home on Staten Island served as a drug-dealing center."
## [318] "Chief Justice John G. Roberts Jr. is well known for his ability to quote obscure case law, but on Monday he invoked the rapper Eminem at the Supreme Court."
## [319] "Mary L. Landrieu, a three term senator, is in the race of her career against the Republican Bill Cassidy as Louisiana voters head to the polls for a runoff vote on Dec. 6."
## [320] "Bank of NewYork Mellon said on Tuesday that it would give a board seat to Edward P. Garden of the hedge fund Trian Fund Management, forestalling a potentially costly proxy fight."
## [321] "The deal for IndCor Properties will make Singapores sovereign wealth fund a major owner of warehouses and distribution centers in the USA."
## [322] "Speaker John A. Boehner on options that Republicans are considering to block President Obamas immigration action."
## [323] "Senator Joseph R. McCarthy was censured 60 years ago today."
## [324] "Jonah M. Kessel filmed a documentary video in China and Myanmar, where jade mines have become a free market for heroin, and tells of the story of an addict he encountered."
## [325] "In a spellbinding letter to Jim Cramer, the activist investor J. Carlo Cannell urged him to cut his pay 70 percent, resign from CNBC and direct his energy to helping your fellow shareholders crawl back from Hades."
## [326] "The Fox affiliate in NewOrleans is taking issue with the use of its news anchors words in a campaign ad for Senator Mary L. Landrieu."
## [327] "NewYorks chokehold case has thrown a volatile element into Loretta E. Lynchs confirmation hearings as USA attorney general."
## [328] "Senator Elizabeth Warrens opposition to a nominee for a role at the Treasury Department demonstrates her blatant political motivation, William D. Cohan writes in the Street Scene column."
## [329] "J. Carlo Cannell wants Jim Cramer to sell TheStreet or take a pay cut and quit CNBC. A sale may be wishful thinking, says Quentin Webb of Reuters Breakingviews. But Mr. Cramers payout is hard to justify."
## [330] "The appellate division of the NewYork State Supreme Court dismissed a lawsuit at the heart of a bitter dispute between the billionaire collector Ronald O. Perelman and the art dealer Larry Gagosian."
## [331] "Photos from Russia, SouthAfrica, India and the USA."
## [332] "Andrea C. Bonomi, the Italian businessman fighting a Chinese investor for Club Mditerrane, raised his bid to $29.65 a share for the French resort operator."
## [333] "President Obama on Friday announced his selection of Ashton B. Carter to lead the Pentagon, White House officials said, embracing a physicist and national security centrist who may advocate a stronger use of American power."
## [334] "Jon Caramanica and A.O. Scott discuss what this update of The Bodyguard gets right about the music business."
## [335] "The famously outspoken, leather-clad architect is being honored this week with Design Miamis inaugural Design Visionary Award and an exhibition at the Bass Museum of Art. In this video, he discusses his career with T."
## [336] "An architect who hopes to save a Paul Rudolph-designed building in Orange County, N.Y., presented his detailed proposal to county leaders there on Friday."
## [337] "Demonstrations focused on fatal police encounters continued on Saturday, with a rally at the Louis H. Pink Houses in Brooklyn to protest the fatal shooting of Akai Gurley there and a grand jurys decision not to indict an officer in the death of Eric Garner."
## [338] "Will the SECs treatment of Bank of America, Peter J. Henning asks in the White Collar Watch column, become the norm for other banks seeking waivers from rules on bad actors?"
## [339] "After years of research, there is genuine hope for reducing the toll exacted by lung cancer, which is the leading cause of cancer deaths in the USA."
## [340] "Senator Charles E. Schumer of NewYork may be Republicans favorite Democrat these days."
## [341] "Recommendations from the American Bankruptcy Institute failed to address derivatives held by nonfinancial institutions that will keep operating, writes Stephen J. Lubben in the In Debt column."
## [342] "Readers expressed outrage and despair in comments on a Friday editorial, It Wasnt Just the Chokehold: Eric Garner, Daniel Pantaleo and Lethal Police Tactics, and on an Op-Ed from Eric L. Adams, We Must Stop Abuse of Black Men."
## [343] "Jessica Bibliowicz will become chairwoman of the Cornell Weill Medical College, effective Jan. 1. Her father, Sanford I. Weill, has been chairman for two decades."
## [344] "The SecondMarket exchange won all but one of the 20 Bitcoin blocks in the governments auction. The venture capitalist Timothy C. Draper, who swept the first auction, won only 2,000 Bitcoins this time."
## [345] "Brian T. Moynihan tells a conference that his bank is expecting trading revenue in the fourth quarter to be down from last quarter and from a year earlier."
## [346] "The revival of A.R. Gurneys play will close this Sunday, before some of its scheduled stars ever get to perform."
## [347] "Jonathan Gruber, the M.IT economist who advised the Obama administration on the Affordable Care Act, apologized on Tuesday for inflammatory comments that have brought negative attention to the law in recent months."
## [348] "The Senate Intelligence Committee has released its long-awaited review on the torture of prisoners held by the Central Intelligence Agency during the George W. Bush administration."
## [349] "Senator Dianne Feinstein, Democrat of California and chair of the Senate Intelligence Committee, headed to the Senate floor Tuesday to talk about the committees newly released report on the torture of prisoners held by the Central Intelligence Agency under the George W. Bush administration."
## [350] "Several Republican members of the Senate Intelligence Committee pushed back against the release of the declassified executive summary of the panels report on the torture of prisoners by the Central Intelligence Agency. They condemned the report on techniques used during the George W. Bush administration as partisan."
## [351] "Just hours after announcing its $8.4 billion acquisition of Cubist Pharmaceuticals, a Delaware judge invalidated patents owned by the antibiotics maker. Merck appears to have been caught up in the M.&.A. exuberance, says Robert Cyran of Reuters Breakingviews."
## [352] "Recent criticism of James J. Cramers compensation from TheStreet.com has painted him as another greedy WallStreet executive looking out for himself but not shareholders."
## [353] "Gov. Chris Christie of NewJersey, who was appointed USA attorney for the state by President George W. Bush in the weeks after the Sept. 11 attacks, seemed not eager on Wednesday to discuss the Senate report on the brutal American interrogation techniques used on terrorism suspects a decade ago."
## [354] "In this lesson we offer three different teaching ideas to engage students in the debate over immigration policy in the USA."
## [355] "The latest appeals court decision will make some insider-trading cases harder, but it does not give a free pass to hedge funds to trade on any confidential information, Peter J. Henning writes in White Collar Watch."
## [356] "The latest NewYorkTimes poll is based on telephone interviews conducted Dec. 4 through 7 with 1,006 adults throughout the USA."
## [357] "The Confucius Peace Prize was first given out in late 2010 as a rejoinder to the Nobel Peace Prize. President Vladimir V. Putin of Russia is a past recipient."
## [358] "Were going to continue to press his nomination forward, Treasury Secretary Jacob J. Lew said. He also spoke about corporate taxes and sanctions on Russia."
## [359] "George R. Goldner will become a private art adviser after 21 years heading the department. Nadine M. Orensteinhas been named to succeed him."
## [360] "Lloyd C. Blankfein, Goldman Sachss chief executive, addressed the antagonism that has been coming out of Washington."
## [361] "Francis J. Shammo, Verizons chief financial officer, said the company planned to continue to invest in its FiOS fiber-optic network and its wireless systems regardless of the outcome of the broadband debate."
## [362] "Senator Dianne Feinstein, chairwoman of the committee that investigated the CIAs interrogation program, offered a live rebuttal as John O. Brennan, the agencys director, responded to the committees findings."
## [363] "The Blackstone Group, the private equity giant run by Stephen A. Schwarzman, is almost finished raising a new fund for energy investments that is expected to exceed $4 billion in assets."
## [364] "OscarSSchafer will succeed the current NewYork Philharmonic chairman, Gary W. Parr, early next year."
## [365] "Friday and the weekend: a struggle for L.G.B.T. protections, more chilly weather, protests on Saturday, and the week in pictures."
## [366] "An A.C.L.U. video features military personnel who objected to the Bush-era torture program."
## [367] "Two new books provide insight into lesser-known stages of Charles M. Schulz and Jack Kirbys careers."
## [368] "On this day in 2000, the Supreme Court ruled 5 to 4 that there would be no additional counting of presidential votes in Florida. 35 days after Election Day, the decision effectively handed the presidency to George W. Bush."
## [369] "Hours after casting his final vote as a member of Congress, Representative John D. Dingell was hospitalized after taking a spill earlier in the week."
## [370] "Jungle Lady is the first single off the NewYork-based act Lion Babes self-titled debut EP."
## [371] "The Securities and Exchange Commission said it would not be able to proceed with the lawsuit because the two main witnesses it intended to call to testify are in Poland and had no plans to return to the USA."
## [372] "The return of Joel D. Lafargue, after a long break."
## [373] "Nominees that didnt make the final cut this year include Nine Inch Nails, Sting, N.W.A, Chic and Kraftwerk."
## [374] "The disturbing statistics on military rape and the fleeting attention of the public led Mary F. Calvert to document the stories of those who survived assaults."
## [375] "James E. Staley, a managing partner at the NewYork hedge fund BlueMountain Capital Management, is expected to be considered for the board during UBSs annual meeting in May, and to join the banks risk committee."
## [376] "Wednesday: The M.T.A. honors its workers, oddly warm weather, and a vigil for Pakistan."
## [377] "President Obama is scheduled to speak about relations with Cuba at 12 p.m. ET."
## [378] "Highlights and video of the remarks by President Raul Castro of Cuba on a diplomatic breakthrough with the USA."
## [379] "Speaker John A. Boehner on Wednesday called President Obamas new policy on Cuba the latest in a long line of mindless concessions to a dictatorship that brutalizes its people and schemed with our enemies."
## [380] "Alan P. Gross prepared his remarks at his attorneys office in Washington on Wednesday."
## [381] "Most Americans support normalizing trade and diplomatic relations with Cuba and consider what happens in the communist nation to be important to the interests of the USA."
## [382] "Photos from Pakistan, Australia, France and the USA."
## [383] "President Obama took the occasion of Alan P. Grosss release not only to acknowledge the interest the Jewish community had taken in Mr. Grosss case, but also to connect his release to the holiday and to Jewish teaching."
## [384] "Curtis L. Buser, who has been interim finance chief for the last seven months, is taking over the job permanently."
## [385] "Speaker John A. Boehner posted a holiday greeting on Thursday, a video poem title Happy Christmas to All, although the Speakers wishes may not extend all the way to the White House."
## [386] "Recommended books for learning more about Cubas history and its relationship to the USA."
## [387] "In a speech on Thursday, Benjamin M. Lawsky, NewYork States top financial regulator, provided the most detail yet on revisions to his proposed Bitcoin rules."
## [388] "The firm promoted John E. Waldron, one of its star deal makers, to become a co-head of its investment bank, succeeding a scion of one of Goldmans most famous families."
## [389] "The disposal is the latest move for Xerox, which has been transformed under the leadership of Ursula M. Burns into a provider of various business services."
## [390] "Speaker John A. Boehner has invited President Obama to address the country on Jan. 20 and give a State of the Union speech before a joint session of the new Republican-controlled Congress."
## [391] "Mark A. Flaherty and Mark O. Winkelman will serve on a board that now has 14 members."
## [392] "Goldman names John E. Waldron co-head of its investment bank. | Regulators deem MetLife too big to fail. | Another big whistle-blower reward in Bank of America case. | London tenants win battle over USA equity firm."
## [393] "After an investigation into conflicts of interest, William C. Erbey has agreed to step down from his position as chairman of Ocwen and four other related companies."
## [394] "Its about non-digital IT."
## [395] "Fred R. Conrad may be best known for his exquisite portraits, but an assignment in Kosovo taught him the value of watching and waiting for the story to come to him."
## [396] "An overhaul of the Freedom of Information Act did not survive the legislative deal-making that produced the spending plan. William D. Cohan laments that loss in the Street Scene column."
## [397] "Its hard to be FDR."
## [398] "Ocwen should look at Clayton Homes, whose credo puts customers first, writes Lawrence A. Cunningham in Another View."
## [399] "Photos from Ukraine, India, Nepal and the USA."
## [400] "NewYorkTimes reporter David W. Dunlap explains how coverage of the Bay of Pigs disaster unfolded."
## [401] "After decades of oppression of people who identify as L.G.B.T., the Cuban government now pays for gender reassignment surgery."
## [402] "Look for the hidden message from Peter A. Collins."
## [403] "The new year will bring cases from the subprime mortgage crisis, a Justice Department decision on an appeal of an insider trading ruling and, perhaps, new accounting frauds, Peter J. Henning writes in the White Collar Watch column."
## [404] "Under the F.D.A.s new policy, any man who has had sex with another man in the last year may not donate."
## [405] "Representative Michael G. Grimm of NewYork insisted last week that he would not resign from his seat. On Monday night, he changed his mind."
## [406] "Speaker John A. Boehner gave his seal of approval to Representative Michael Grimms resignation."
## [407] "David E. Sanger, chief Washington correspondent for NewYorkTimes, recalled a visit to the country 25 years ago."
## [408] "Former President George H. W. Bush was released from a Houston hospital where he has been staying since last week after experiencing shortness of breath."
## [409] "Speaker John A. Boehner expressed his support for Representative Steve Scalise, who spoke at a conference for white supremacists in 2002."
## [1] "Remaining #\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+# terms in Headline: "
## pattern .n
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## [1] " consider cleaning if relevant to problem domain; geography name; .n > 1"
## [1] "Remaining #\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+# terms in Snippet: "
## pattern .n
## 2 New Atheist 1
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## [1] " consider cleaning if relevant to problem domain; geography name; .n > 1"
## [1] "Remaining #\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+# terms in Abstract: "
## pattern .n
## 2 New Atheist 1
## 3 New Brain 1
## 4 New Centurys 1
## 5 New Cuban 1
## 6 New Deal 1
## 7 New Enterprise 1
## 8 New Era 1
## 9 New Health 1
## 10 New Rich 1
## 11 New Things 1
## 28 Saint-Gobain 1
## 29 St. Andrew 1
## 30 St. Baldricks 1
## 31 St. Mary 1
## 32 St. Y 1
## [1] " consider cleaning if relevant to problem domain; geography name; .n > 1"
## [1] "Remaining #\\b(N|S|E|W|C)( |\\.)(\\w)+# terms in Headline: "
## pattern .n
## 1 C Card 1
## 2 E and 1
## 3 N Cheez 1
## 4 N Weaver 1
## 5 W Hotels 1
## [1] "Remaining #\\b(N|S|E|W|C)( |\\.)(\\w)+# terms in Snippet: "
## pattern .n
## 1 N.Y 5
## 2 C Concourse 1
## 3 C.F 1
## 4 C.H 1
## 5 C.L 1
## 6 C.M 1
## 7 E and 1
## 8 E is 1
## 9 E.E 1
## 10 N.C 1
## 11 N.W 1
## 12 S and 1
## 13 S.I 1
## 14 S.S 1
## 15 W Hotels 1
## 16 W Retreat 1
## [1] "Remaining #\\b(N|S|E|W|C)( |\\.)(\\w)+# terms in Abstract: "
## pattern .n
## 1 N.Y 5
## 2 C Concourse 1
## 3 C.F 1
## 4 C.H 1
## 5 C.L 1
## 6 C.M 1
## 7 E and 1
## 8 E is 1
## 9 E.E 1
## 10 N.C 1
## 11 N.W 1
## 12 S and 1
## 13 S.I 1
## 14 S.S 1
## 15 W Hotels 1
## 16 W Retreat 1
## [1] "Remaining #\\b(North|South|East|West|Central)( |\\.)(\\w)+# terms in Headline: "
## pattern .n
## 1 Central Bank 3
## 2 East Side 3
## 3 North West 2
## 4 West to 2
## 5 Central Bankers 1
## 6 Central Cast 1
## 7 Central Italy 1
## 8 Central Role 1
## 9 East Africa 1
## 10 East Berlin 1
## 11 East London 1
## 12 East Poland 1
## 13 East Rivals 1
## 14 East Spring 1
## 15 North Transfer 1
## 16 North Views 1
## 17 South Lawn 1
## 20 South of 1
## 18 South Pacific 1
## 19 South Street 1
## 21 South to 1
## 22 West 11th 1
## 23 West Africa 1
## 24 West African 1
## 25 West Coast 1
## [1] "Remaining #\\b(North|South|East|West|Central)( |\\.)(\\w)+# terms in Snippet: "
## pattern .n
## 1 East Side 12
## 2 Central Bank 8
## 3 West Africa 8
## 4 Central Intelligence 5
## 5 East Coast 5
## 6 West Bank 5
## 7 East and 4
## 8 West Coast 4
## 9 South Beach 3
## 10 West Side 3
## 11 East End 2
## 12 North Portico 2
## 13 South Africas 2
## 14 West African 2
## 16 West and 2
## 15 West Village 2
## 23 Central and 1
## 24 Central bankers 1
## 17 Central Banks 1
## 25 Central business 1
## 18 Central District 1
## 26 Central show 1
## 19 Central Television 1
## 20 Central Terminal 1
## 21 Central Time 1
## 22 Central Valley 1
## 27 East 55th 1
## 28 East Africa 1
## 29 East Asia 1
## 30 East Berliners 1
## 31 East Christians 1
## 41 East correspondent 1
## 32 East Flatbush 1
## 33 East German 1
## 34 East Germans 1
## 42 East haunts 1
## 35 East Hollywood 1
## 43 East in 1
## 36 East Jerusalem 1
## 37 East Poland 1
## 38 East River 1
## 39 East Room 1
## 40 East Timor 1
## 44 North African 1
## 45 North Americas 1
## 46 North Carolinas 1
## 47 North English 1
## 50 North gates 1
## 51 North to 1
## 48 North Vietnamese 1
## 49 North Water 1
## 58 South as 1
## 52 South Asia 1
## 53 South Asian 1
## 54 South Dakotas 1
## 55 South Florida 1
## 59 South in 1
## 60 South of 1
## 56 South Rhodesias 1
## 57 South Will 1
## 61 West 47th 1
## 62 West 50th 1
## 63 West Florrissant 1
## 67 West in 1
## 68 West is 1
## 64 West Rivers 1
## 65 West Sider 1
## 66 West Wing 1
## [1] "Remaining #\\b(North|South|East|West|Central)( |\\.)(\\w)+# terms in Abstract: "
## pattern .n
## 1 East Side 12
## 2 Central Bank 8
## 3 West Africa 8
## 4 Central Intelligence 6
## 5 East Coast 5
## 6 West Bank 5
## 7 East and 4
## 8 West Coast 4
## 9 South Beach 3
## 10 West Side 3
## 11 East End 2
## 12 North Portico 2
## 13 South Africas 2
## 14 West African 2
## 16 West and 2
## 15 West Village 2
## 23 Central and 1
## 24 Central bankers 1
## 17 Central Banks 1
## 25 Central business 1
## 18 Central District 1
## 26 Central show 1
## 19 Central Television 1
## 20 Central Terminal 1
## 21 Central Time 1
## 22 Central Valley 1
## 27 East 55th 1
## 28 East Africa 1
## 29 East Asia 1
## 30 East Berliners 1
## 31 East Christians 1
## 41 East correspondent 1
## 32 East Flatbush 1
## 33 East German 1
## 34 East Germans 1
## 42 East haunts 1
## 35 East Hollywood 1
## 43 East in 1
## 36 East Jerusalem 1
## 37 East Poland 1
## 38 East River 1
## 39 East Room 1
## 40 East Timor 1
## 44 North African 1
## 45 North Americas 1
## 46 North Carolinas 1
## 47 North English 1
## 50 North gates 1
## 51 North to 1
## 48 North Vietnamese 1
## 49 North Water 1
## 58 South as 1
## 52 South Asia 1
## 53 South Asian 1
## 54 South Dakotas 1
## 55 South Florida 1
## 59 South in 1
## 60 South of 1
## 56 South Rhodesias 1
## 57 South Will 1
## 61 West 47th 1
## 62 West 50th 1
## 63 West Florrissant 1
## 67 West in 1
## 68 West is 1
## 64 West Rivers 1
## 65 West Sider 1
## 66 West Wing 1
## label step_major
## 3 extract.features_process.text 3
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed
## 3 0 43.980 58.55 14.57
## 4 1 58.551 NA NA
## [1] "Remaining compound terms in Headline: "
## [1] "Remaining compound terms in Snippet: "
## [1] "Remaining compound terms in Abstract: "
## label step_major
## 4 extract.features_process.text_reporting_compound_terms 3
## 5 extract.features_build.corpus 4
## step_minor bgn end elapsed
## 4 1 58.551 58.56 0.009
## 5 0 58.561 NA NA
## [1] "Building glb_corpus_lst..."
## label step_major step_minor bgn end
## 5 extract.features_build.corpus 4 0 58.561 97.397
## 6 extract.features_extract.DTM 5 0 97.398 NA
## elapsed
## 5 38.836
## 6 NA
## [1] "Extracting TfIDf terms for Headline..."## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0)## [1] "Extracting TfIDf terms for Snippet..."## Warning in weighting(x): empty document(s): character(0) character(0)## [1] "Extracting TfIDf terms for Abstract..."## Warning in weighting(x): empty document(s): character(0) character(0)## label step_major step_minor bgn end
## 6 extract.features_extract.DTM 5 0 97.398 110.766
## 7 extract.features_report.DTM 6 0 110.767 NA
## elapsed
## 6 13.368
## 7 NA
## [1] "Reporting TfIDf terms for Headline..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 8402, terms: 9218)>>
## Non-/sparse entries: 44246/77405390
## Sparsity : 100%
## Maximal term length: 31
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)
## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 8402, terms: 28)>>
## Non-/sparse entries: 4546/230710
## Sparsity : 98%
## Maximal term length: 10
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
## colorcol_name = "in.sprs"): converting in.sprs to class:factor
## Warning in loop_apply(n, do.ply): Removed 6 rows containing missing values
## (geom_path).## Warning in loop_apply(n, do.ply): Removed 6 rows containing missing values
## (geom_path).
## [1] "Reporting TfIDf terms for Snippet..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 8402, terms: 13694)>>
## Non-/sparse entries: 105089/114951899
## Sparsity : 100%
## Maximal term length: 25
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)
## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 8402, terms: 22)>>
## Non-/sparse entries: 8285/176559
## Sparsity : 96%
## Maximal term length: 10
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
## colorcol_name = "in.sprs"): converting in.sprs to class:factor
## Warning in loop_apply(n, do.ply): Removed 6 rows containing missing values
## (geom_path).## Warning in loop_apply(n, do.ply): Removed 6 rows containing missing values
## (geom_path).
## [1] "Reporting TfIDf terms for Abstract..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 8402, terms: 13738)>>
## Non-/sparse entries: 105466/115321210
## Sparsity : 100%
## Maximal term length: 112
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)
## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 8402, terms: 22)>>
## Non-/sparse entries: 8297/176547
## Sparsity : 96%
## Maximal term length: 10
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
## colorcol_name = "in.sprs"): converting in.sprs to class:factor
## Warning in loop_apply(n, do.ply): Removed 6 rows containing missing values
## (geom_path).## Warning in loop_apply(n, do.ply): Removed 6 rows containing missing values
## (geom_path).
## label step_major step_minor bgn end
## 7 extract.features_report.DTM 6 0 110.767 165.267
## 8 extract.features_bind.DTM 7 0 165.267 NA
## elapsed
## 7 54.5
## 8 NA
## [1] "Binding DTM for Headline..."
## [1] "Binding DTM for Snippet..."
## [1] "Binding DTM for Abstract..."
## label step_major step_minor bgn end elapsed
## 8 extract.features_bind.DTM 7 0 165.267 165.364 0.097
## 9 extract.features_bind.DXM 8 0 165.364 NA NA## Warning in rm(log_X_df, txt_X_df): object 'log_X_df' not found
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
glb_full_DTM_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'corpus_lst' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 extract.features_bind.DXM 8 0 165.364 259.624 94.263
## 10 extract.features_end 9 0 259.632 NA NAmyplt_chunk(extract.features_chunk_df)## label step_major
## 9 extract.features_bind.DXM 8
## 7 extract.features_report.DTM 6
## 5 extract.features_build.corpus 4
## 3 extract.features_process.text 3
## 6 extract.features_extract.DTM 5
## 2 extract.features_factorize.str.vars 2
## 8 extract.features_bind.DTM 7
## 1 extract.features_bgn 1
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed duration
## 9 0 165.364 259.624 94.263 94.260
## 7 0 110.767 165.267 54.500 54.500
## 5 0 58.561 97.397 38.836 38.836
## 3 0 43.980 58.550 14.570 14.570
## 6 0 97.398 110.766 13.368 13.368
## 2 0 42.466 43.980 1.514 1.514
## 8 0 165.267 165.364 0.097 0.097
## 1 0 42.453 42.465 0.013 0.012
## 4 1 58.551 58.560 0.009 0.009
## [1] "Total Elapsed Time: 259.624 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 6 extract.features 3 0 42.404 262.193 219.79
## 7 cluster.data 4 0 262.194 NA NAStep 4.0: cluster data
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stophere; sav_allobs_df <- glb_allobs_df;
print("Clustering features: ")
print(cluster_vars <- grep("[HSA]\\.[PT]\\.", names(glb_allobs_df), value=TRUE))
#print(cluster_vars <- grep("[HSA]\\.", names(glb_allobs_df), value=TRUE))
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
for (myCategory in c("##", "Business#Business Day#Dealbook", "OpEd#Opinion#",
"Styles#U.S.#", "Business#Technology#", "Science#Health#",
"Culture#Arts#")) {
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df$myCategory == myCategory, ]
dstns_dist <- dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_mtrx <- as.matrix(dstns_dist)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
clusters <- hclust(dstns_dist, method = "ward.D2")
#plot(clusters, labels=NULL, hang=-1)
myplclust(clusters, lab.col=unclass(ctgry_allobs_df[, glb_rsp_var]))
#clusterGroups = cutree(clusters, k=7)
clusterGroups <- cutreeDynamic(clusters, minClusterSize=20, method="tree", deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#print(ctgry_allobs_df[which(clusterGroups == 1), c("UniqueID", "Popular", "Headline")])
#print(ctgry_allobs_df[(clusterGroups == 1) & !is.na(ctgry_allobs_df$Popular) & (ctgry_allobs_df$Popular==1), c("UniqueID", "Popular", "Headline")])
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#summary(factor(clusterGroups))
# clusterGroups <- clusterGroups +
# 100 * # has to be > max(table(glb_allobs_df$myCategory.fctr) / minClusterSize=20)
# which(levels(glb_allobs_df$myCategory.fctr) == myCategory)
# table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
# add to glb_allobs_df - then split the data again
glb_allobs_df[glb_allobs_df$myCategory==myCategory,]$.clusterid <- clusterGroups
#print(unique(glb_allobs_df$.clusterid))
#print(glb_feats_df[glb_feats_df$id == ".clusterid.fctr", ])
}
ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
mycreate_sqlxtab_df(glb_allobs_df,
c("myCategory", ".clusterid", glb_rsp_var)))
ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
myCategory + .clusterid ~
Popular.fctr, sum, value.var=".n"))
print(ctgry_cast_df)
#print(orderBy(~ myCategory -Y -NA, ctgry_cast_df))
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_clst.csv"),
# row.names=FALSE)
print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df$.clusterid,
glb_allobs_df[, glb_rsp_var],
useNA="ifany"))
# dsp_obs(.clusterid=1, myCategory="OpEd#Opinion#",
# cols=c("UniqueID", "Popular", "myCategory", ".clusterid", "Headline"),
# all=TRUE)
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
".clusterid")
glb_interaction_only_features["myCategory.fctr"] <- c(".clusterid.fctr")
}## Loading required package: proxy
##
## Attaching package: 'proxy'
##
## The following objects are masked from 'package:stats':
##
## as.dist, dist
##
## The following object is masked from 'package:base':
##
## as.matrix
##
## Loading required package: dynamicTreeCut## [1] "Clustering features: "
## [1] "H.T.X2014" "H.T.X2015"
## [3] "H.T.art" "H.T.bank"
## [5] "H.T.big" "H.T.billion"
## [7] "H.T.busi" "H.T.china"
## [9] "H.T.daili" "H.T.day"
## [11] "H.T.deal" "H.T.fashion"
## [13] "H.T.first" "H.T.make"
## [15] "H.T.morn" "H.T.new"
## [17] "H.T.news" "H.T.newyork"
## [19] "H.T.obama" "H.T.pictur"
## [21] "H.T.polit" "H.T.report"
## [23] "H.T.say" "H.T.springsumm"
## [25] "H.T.take" "H.T.test"
## [27] "H.T.today" "H.T.week"
## [29] "S.T.articl" "S.T.can"
## [31] "S.T.compani" "S.T.day"
## [33] "S.T.fashion" "S.T.first"
## [35] "S.T.intern" "S.T.make"
## [37] "S.T.new" "S.T.newyork"
## [39] "S.T.newyorktim" "S.T.one"
## [41] "S.T.presid" "S.T.report"
## [43] "S.T.said" "S.T.share"
## [45] "S.T.show" "S.T.take"
## [47] "S.T.time" "S.T.week"
## [49] "S.T.will" "S.T.year"
## [51] "A.T.articl" "A.T.can"
## [53] "A.T.compani" "A.T.day"
## [55] "A.T.fashion" "A.T.first"
## [57] "A.T.intern" "A.T.make"
## [59] "A.T.new" "A.T.newyork"
## [61] "A.T.newyorktim" "A.T.one"
## [63] "A.T.presid" "A.T.report"
## [65] "A.T.said" "A.T.share"
## [67] "A.T.show" "A.T.take"
## [69] "A.T.time" "A.T.week"
## [71] "A.T.will" "A.T.year"
## [73] "H.T.clip" "H.T.ebola"
## [75] "H.T.get" "H.T.newyorktim"
## [77] "H.T.read" "H.T.word"
## [79] "H.P.http" "H.P.year.colon"
## [81] "H.P.daily.clip.report" "H.P.fashion.week"
## [83] "H.P.first.draft" "H.P.facts.figures"
## [85] "H.P.friday.night.music" "H.P.no.comment.colon"
## [87] "H.P.on.this.day" "H.P.quandary"
## [89] "H.P.readers.respond" "H.P.recap.colon"
## [91] "H.P.s.notebook" "H.P.today.in.politic"
## [93] "H.P.today.in.smallbusiness" "H.P.verbatim.colon"
## [95] "H.P.what.we.are" "S.T.appear"
## [97] "S.T.archiv" "S.T.diari"
## [99] "S.T.herald" "S.T.obama"
## [101] "S.T.photo" "S.T.senat"
## [103] "S.T.tribun" "S.T.word"
## [105] "S.P.http" "S.P.year.colon"
## [107] "S.P.daily.clip.report" "S.P.fashion.week"
## [109] "S.P.first.draft" "S.P.metropolitan.diary.colon"
## [111] "A.T.appear" "A.T.archiv"
## [113] "A.T.diari" "A.T.herald"
## [115] "A.T.obama" "A.T.photo"
## [117] "A.T.senat" "A.T.tribun"
## [119] "A.T.word" "A.P.http"
## [121] "A.P.year.colon" "A.P.daily.clip.report"
## [123] "A.P.fashion.week" "A.P.first.draft"
## [125] "A.P.metropolitan.diary.colon"
## [1] "max distance(1.0000) pair:"
## UniqueID Popular myCategory
## 105 105 0 ##
## 95 95 1 ##
## Headline
## 105 When Buildings Are Political, Should Architects Be Politicians?
## 95 Phrases We Love Too Much
## H.T.X2014 H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi
## 105 0 0 0 0 0 0 0
## 95 0 0 0 0 0 0 0
## H.T.china H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make
## 105 0 0 0 0 0 0 0
## 95 0 0 0 0 0 0 0
## H.T.morn H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit
## 105 0 0 0 0 0 0 1.532407
## 95 0 0 0 0 0 0 0.000000
## H.T.report H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week
## 105 0 0 0 0 0 0 0
## 95 0 0 0 0 0 0 0
## S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first
## 105 0 0 0 0 0 0
## 95 0 0 0 0 0 0
## S.T.intern S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one
## 105 0 0.0000000 0 0 0 0
## 95 0 0.8094347 0 0 0 0
## S.T.presid S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time
## 105 0 0 0 0 0 0.3554158 0
## 95 0 0 0 0 0 0.0000000 0
## S.T.week S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day
## 105 0 0 0 0 0 0 0
## 95 0 0 0 0 0 0 0
## A.T.fashion A.T.first A.T.intern A.T.make A.T.new A.T.newyork
## 105 0 0 0 0.000000 0 0
## 95 0 0 0 0.808607 0 0
## A.T.newyorktim A.T.one A.T.presid A.T.report A.T.said A.T.share
## 105 0 0 0 0 0 0
## 95 0 0 0 0 0 0
## A.T.show A.T.take A.T.time A.T.week A.T.will A.T.year H.T.clip
## 105 0 0.3550306 0 0 0 0 0
## 95 0 0.0000000 0 0 0 0 0
## H.T.ebola H.T.get H.T.newyorktim H.T.read H.T.word H.P.http
## 105 0 0 0 0 0 0
## 95 0 0 0 0 0 0
## H.P.year.colon H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 105 0 0 0 0
## 95 0 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 105 0 0 0
## 95 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 105 0 0 0 0
## 95 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 105 0 0 0
## 95 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 105 0 0 0 0 0
## 95 0 0 0 0 0
## S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word S.P.http
## 105 0 0 0 0 0 0 0
## 95 0 0 0 0 0 0 0
## S.P.year.colon S.P.daily.clip.report S.P.fashion.week S.P.first.draft
## 105 0 0 0 0
## 95 0 0 0 0
## S.P.metropolitan.diary.colon A.T.appear A.T.archiv A.T.diari
## 105 0 0 0 0
## 95 0 0 0 0
## A.T.herald A.T.obama A.T.photo A.T.senat A.T.tribun A.T.word A.P.http
## 105 0 0 0 0 0 0 0
## 95 0 0 0 0 0 0 0
## A.P.year.colon A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 105 0 0 0 0
## 95 0 0 0 0
## A.P.metropolitan.diary.colon
## 105 0
## 95 0
## [1] "min distance(-0.0000) pair:"
## UniqueID Popular myCategory Headline H.T.X2014
## 95 95 1 ## Phrases We Love Too Much 0
## 3881 3881 0 ## The Power of Feeling Bad 0
## H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi H.T.china
## 95 0 0 0 0 0 0 0
## 3881 0 0 0 0 0 0 0
## H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make H.T.morn
## 95 0 0 0 0 0 0 0
## 3881 0 0 0 0 0 0 0
## H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit
## 95 0 0 0 0 0 0
## 3881 0 0 0 0 0 0
## H.T.report H.T.say H.T.springsumm H.T.take H.T.test H.T.today
## 95 0 0 0 0 0 0
## 3881 0 0 0 0 0 0
## H.T.week S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first
## 95 0 0 0 0 0 0 0
## 3881 0 0 0 0 0 0 0
## S.T.intern S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one
## 95 0 0.8094347 0 0 0 0
## 3881 0 0.6938011 0 0 0 0
## S.T.presid S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time
## 95 0 0 0 0 0 0 0
## 3881 0 0 0 0 0 0 0
## S.T.week S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day
## 95 0 0 0 0 0 0 0
## 3881 0 0 0 0 0 0 0
## A.T.fashion A.T.first A.T.intern A.T.make A.T.new A.T.newyork
## 95 0 0 0 0.8086070 0 0
## 3881 0 0 0 0.6930917 0 0
## A.T.newyorktim A.T.one A.T.presid A.T.report A.T.said A.T.share
## 95 0 0 0 0 0 0
## 3881 0 0 0 0 0 0
## A.T.show A.T.take A.T.time A.T.week A.T.will A.T.year H.T.clip
## 95 0 0 0 0 0 0 0
## 3881 0 0 0 0 0 0 0
## H.T.ebola H.T.get H.T.newyorktim H.T.read H.T.word H.P.http
## 95 0 0 0 0 0 0
## 3881 0 0 0 0 0 0
## H.P.year.colon H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 95 0 0 0 0
## 3881 0 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 95 0 0 0
## 3881 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 95 0 0 0 0
## 3881 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 95 0 0 0
## 3881 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 95 0 0 0 0 0
## 3881 0 0 0 0 0
## S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word S.P.http
## 95 0 0 0 0 0 0 0
## 3881 0 0 0 0 0 0 0
## S.P.year.colon S.P.daily.clip.report S.P.fashion.week S.P.first.draft
## 95 0 0 0 0
## 3881 0 0 0 0
## S.P.metropolitan.diary.colon A.T.appear A.T.archiv A.T.diari
## 95 0 0 0 0
## 3881 0 0 0 0
## A.T.herald A.T.obama A.T.photo A.T.senat A.T.tribun A.T.word A.P.http
## 95 0 0 0 0 0 0 0
## 3881 0 0 0 0 0 0 0
## A.P.year.colon A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 95 0 0 0 0
## 3881 0 0 0 0
## A.P.metropolitan.diary.colon
## 95 0
## 3881 0
## [1] "max distance(1.0000) pair:"
## UniqueID Popular myCategory
## 29 29 0 Business#Business Day#Dealbook
## 27 27 0 Business#Business Day#Dealbook
## Headline H.T.X2014
## 29 Heineken to Sell Mexican Packaging Unit to Crown Holdings 0
## 27 Fund Manager Neil Woodford Sells HSBC Stake on Risk of Fines 0
## H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi H.T.china
## 29 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0
## H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make H.T.morn
## 29 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0
## H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit H.T.report
## 29 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0
## H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week S.T.articl
## 29 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0
## S.T.can S.T.compani S.T.day S.T.fashion S.T.first S.T.intern
## 29 0.3104759 0 0 0 0 0
## 27 0.0000000 0 0 0 0 0
## S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one S.T.presid
## 29 0.3237739 0 0 0 0 0
## 27 0.0000000 0 0 0 0 0
## S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time S.T.week
## 29 0 0.0000000 0 0 0 0 0
## 27 0 0.3290046 0 0 0 0 0
## S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day A.T.fashion
## 29 0 0 0 0.3104759 0 0 0
## 27 0 0 0 0.0000000 0 0 0
## A.T.first A.T.intern A.T.make A.T.new A.T.newyork A.T.newyorktim
## 29 0 0 0.3234428 0 0 0
## 27 0 0 0.0000000 0 0 0
## A.T.one A.T.presid A.T.report A.T.said A.T.share A.T.show A.T.take
## 29 0 0 0 0.0000000 0 0 0
## 27 0 0 0 0.3290046 0 0 0
## A.T.time A.T.week A.T.will A.T.year H.T.clip H.T.ebola H.T.get
## 29 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0
## H.T.newyorktim H.T.read H.T.word H.P.http H.P.year.colon
## 29 0 0 0 0 0
## 27 0 0 0 0 0
## H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 29 0 0 0
## 27 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 29 0 0 0
## 27 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 29 0 0 0 0
## 27 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 29 0 0 0
## 27 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 29 0 0 0 0 0
## 27 0 0 0 0 0
## S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word S.P.http
## 29 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0
## S.P.year.colon S.P.daily.clip.report S.P.fashion.week S.P.first.draft
## 29 0 0 0 0
## 27 0 0 0 0
## S.P.metropolitan.diary.colon A.T.appear A.T.archiv A.T.diari A.T.herald
## 29 0 0 0 0 0
## 27 0 0 0 0 0
## A.T.obama A.T.photo A.T.senat A.T.tribun A.T.word A.P.http
## 29 0 0 0 0 0 0
## 27 0 0 0 0 0 0
## A.P.year.colon A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 29 0 0 0 0
## 27 0 0 0 0
## A.P.metropolitan.diary.colon
## 29 0
## 27 0
## [1] "min distance(-0.0000) pair:"
## UniqueID Popular myCategory
## 486 486 0 Business#Business Day#Dealbook
## 666 666 0 Business#Business Day#Dealbook
## Headline H.T.X2014
## 486 Pension Funds Join Lawsuit on High-Frequency Trading 0
## 666 Coinbase Extends Bitcoin Access to International Customers 0
## H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi H.T.china
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make H.T.morn
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit H.T.report
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week S.T.articl
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## S.T.can S.T.compani S.T.day S.T.fashion S.T.first S.T.intern S.T.make
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## S.T.new S.T.newyork S.T.newyorktim S.T.one S.T.presid S.T.report
## 486 0 0 0 0.2477271 0 0
## 666 0 0 0 0.2346889 0 0
## S.T.said S.T.share S.T.show S.T.take S.T.time S.T.week S.T.will
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## S.T.year A.T.articl A.T.can A.T.compani A.T.day A.T.fashion A.T.first
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## A.T.intern A.T.make A.T.new A.T.newyork A.T.newyorktim A.T.one
## 486 0 0 0 0 0 0.2475176
## 666 0 0 0 0 0 0.2344903
## A.T.presid A.T.report A.T.said A.T.share A.T.show A.T.take A.T.time
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## A.T.week A.T.will A.T.year H.T.clip H.T.ebola H.T.get H.T.newyorktim
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## H.T.read H.T.word H.P.http H.P.year.colon H.P.daily.clip.report
## 486 0 0 0 0 0
## 666 0 0 0 0 0
## H.P.fashion.week H.P.first.draft H.P.facts.figures
## 486 0 0 0
## 666 0 0 0
## H.P.friday.night.music H.P.no.comment.colon H.P.on.this.day
## 486 0 0 0
## 666 0 0 0
## H.P.quandary H.P.readers.respond H.P.recap.colon H.P.s.notebook
## 486 0 0 0 0
## 666 0 0 0 0
## H.P.today.in.politic H.P.today.in.smallbusiness H.P.verbatim.colon
## 486 0 0 0
## 666 0 0 0
## H.P.what.we.are S.T.appear S.T.archiv S.T.diari S.T.herald S.T.obama
## 486 0 0 0 0 0 0
## 666 0 0 0 0 0 0
## S.T.photo S.T.senat S.T.tribun S.T.word S.P.http S.P.year.colon
## 486 0 0 0 0 0 0
## 666 0 0 0 0 0 0
## S.P.daily.clip.report S.P.fashion.week S.P.first.draft
## 486 0 0 0
## 666 0 0 0
## S.P.metropolitan.diary.colon A.T.appear A.T.archiv A.T.diari
## 486 0 0 0 0
## 666 0 0 0 0
## A.T.herald A.T.obama A.T.photo A.T.senat A.T.tribun A.T.word A.P.http
## 486 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0
## A.P.year.colon A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 486 0 0 0 0
## 666 0 0 0 0
## A.P.metropolitan.diary.colon
## 486 0
## 666 0
## [1] "max distance(1.0000) pair:"
## UniqueID Popular myCategory
## 20 20 0 OpEd#Opinion#
## 9 9 1 OpEd#Opinion#
## Headline
## 20 Is Rational Water Management Finally Emerging in Dry California?
## 9 Why New York City Opposed Abe Lincoln
## H.T.X2014 H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi
## 20 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## H.T.china H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make
## 20 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## H.T.morn H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit
## 20 0 0 0 0.0000000 0 0 0
## 9 0 0 0 0.9835152 0 0 0
## H.T.report H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week
## 20 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first S.T.intern
## 20 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one S.T.presid
## 20 0 0 0 0 0 0.0000000
## 9 0 0 0 0 0 0.3831438
## S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time S.T.week
## 20 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day A.T.fashion
## 20 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## A.T.first A.T.intern A.T.make A.T.new A.T.newyork A.T.newyorktim
## 20 0 0 0 0 0 0
## 9 0 0 0 0 0 0
## A.T.one A.T.presid A.T.report A.T.said A.T.share A.T.show A.T.take
## 20 0 0.0000000 0 0 0 0 0
## 9 0 0.3831438 0 0 0 0 0
## A.T.time A.T.week A.T.will A.T.year H.T.clip H.T.ebola H.T.get
## 20 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## H.T.newyorktim H.T.read H.T.word H.P.http H.P.year.colon
## 20 0 0 0 0 0
## 9 0 0 0 0 0
## H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 20 0 0 0
## 9 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 20 0 0 0
## 9 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 20 0 0 0 0
## 9 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 20 0 0 0
## 9 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 20 0 0 0 0 0
## 9 0 0 0 0 0
## S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word S.P.http
## 20 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## S.P.year.colon S.P.daily.clip.report S.P.fashion.week S.P.first.draft
## 20 0 0 0 0
## 9 0 0 0 0
## S.P.metropolitan.diary.colon A.T.appear A.T.archiv A.T.diari A.T.herald
## 20 0 0 0 0 0
## 9 0 0 0 0 0
## A.T.obama A.T.photo A.T.senat A.T.tribun A.T.word A.P.http
## 20 0 0 0 0 0 0
## 9 0 0 0 0 0 0
## A.P.year.colon A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 20 0 0 0 0
## 9 0 0 0 0
## A.P.metropolitan.diary.colon
## 20 0
## 9 0
## [1] "min distance(-0.0000) pair:"
## UniqueID Popular myCategory
## 371 371 1 OpEd#Opinion#
## 1782 1782 1 OpEd#Opinion#
## Headline H.T.X2014
## 371 A Whale of a Recovery for California's Blue Whales 0
## 1782 Grumbling About, and Watching, Football 0
## H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi H.T.china
## 371 0 0 0 0 0 0 0
## 1782 0 0 0 0 0 0 0
## H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make H.T.morn
## 371 0 0 0 0 0 0 0
## 1782 0 0 0 0 0 0 0
## H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit
## 371 0 0 0 0 0 0
## 1782 0 0 0 0 0 0
## H.T.report H.T.say H.T.springsumm H.T.take H.T.test H.T.today
## 371 0 0 0 0 0 0
## 1782 0 0 0 0 0 0
## H.T.week S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first
## 371 0 0 0 0 0 0 0
## 1782 0 0 0 0 0 0 0
## S.T.intern S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one
## 371 0 0 0 0 0 0
## 1782 0 0 0 0 0 0
## S.T.presid S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time
## 371 0 0 0 0 0 0 0
## 1782 0 0 0 0 0 0 0
## S.T.week S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day
## 371 0 0 0 0 0 0 0
## 1782 0 0 0 0 0 0 0
## A.T.fashion A.T.first A.T.intern A.T.make A.T.new A.T.newyork
## 371 0 0 0 0 0 0
## 1782 0 0 0 0 0 0
## A.T.newyorktim A.T.one A.T.presid A.T.report A.T.said A.T.share
## 371 0 0 0 0 0 0
## 1782 0 0 0 0 0 0
## A.T.show A.T.take A.T.time A.T.week A.T.will A.T.year H.T.clip
## 371 0 0 0 0 0 0 0
## 1782 0 0 0 0 0 0 0
## H.T.ebola H.T.get H.T.newyorktim H.T.read H.T.word H.P.http
## 371 0 0 0 0 0 0
## 1782 0 0 0 0 0 0
## H.P.year.colon H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 371 0 0 0 0
## 1782 0 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 371 0 0 0
## 1782 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 371 0 0 0 0
## 1782 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 371 0 0 0
## 1782 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 371 0 0 0.6390128 0 0
## 1782 0 0 1.1502230 0 0
## S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word S.P.http
## 371 0 0 0 0 0 0 0
## 1782 0 0 0 0 0 0 0
## S.P.year.colon S.P.daily.clip.report S.P.fashion.week S.P.first.draft
## 371 0 0 0 0
## 1782 0 0 0 0
## S.P.metropolitan.diary.colon A.T.appear A.T.archiv A.T.diari
## 371 0 0.6390128 0 0
## 1782 0 1.1502230 0 0
## A.T.herald A.T.obama A.T.photo A.T.senat A.T.tribun A.T.word A.P.http
## 371 0 0 0 0 0 0 0
## 1782 0 0 0 0 0 0 0
## A.P.year.colon A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 371 0 0 0 0
## 1782 0 0 0 0
## A.P.metropolitan.diary.colon
## 371 0
## 1782 0
## [1] "max distance(1.0000) pair:"
## UniqueID Popular myCategory
## 87 87 1 Styles#U.S.#
## 157 157 1 Styles#U.S.#
## Headline H.T.X2014
## 87 Homework and Consequences 0
## 157 Weekly Quandary: Should You Push a Child to Play a Sport? 0
## H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi H.T.china
## 87 0 0 0 0 0 0 0
## 157 0 0 0 0 0 0 0
## H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make H.T.morn
## 87 0 0 0 0 0 0 0
## 157 0 0 0 0 0 0 0
## H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit H.T.report
## 87 0 0 0 0 0 0 0
## 157 0 0 0 0 0 0 0
## H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week
## 87 0 0 0 0 0 0.0000000
## 157 0 0 0 0 0 0.8266169
## S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first
## 87 0 0 0 0 0 0
## 157 0 0 0 0 0 0
## S.T.intern S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one
## 87 0 0.0000000 0 0 0 0
## 157 0 0.4415098 0 0 0 0
## S.T.presid S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time
## 87 0 0 0 0 0 0 0
## 157 0 0 0 0 0 0 0
## S.T.week S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day
## 87 0 0 0 0 0 0 0
## 157 0 0 0 0 0 0 0
## A.T.fashion A.T.first A.T.intern A.T.make A.T.new A.T.newyork
## 87 0 0 0 0.0000000 0 0
## 157 0 0 0 0.4410583 0 0
## A.T.newyorktim A.T.one A.T.presid A.T.report A.T.said A.T.share
## 87 0 0 0 0 0 0
## 157 0 0 0 0 0 0
## A.T.show A.T.take A.T.time A.T.week A.T.will A.T.year H.T.clip
## 87 0 0 0 0 0 0 0
## 157 0 0 0 0 0 0 0
## H.T.ebola H.T.get H.T.newyorktim H.T.read H.T.word H.P.http
## 87 0 0 0 0 0 0
## 157 0 0 0 0 0 0
## H.P.year.colon H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 87 0 0 0 0
## 157 0 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 87 0 0 0
## 157 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 87 0 0 0 0
## 157 0 1 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 87 0 0 0
## 157 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 87 0 0 0 0 0
## 157 0 0 0 0 0
## S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word S.P.http
## 87 0 0 0 0 0 0 0
## 157 0 0 0 0 0 0 0
## S.P.year.colon S.P.daily.clip.report S.P.fashion.week S.P.first.draft
## 87 0 0 0 0
## 157 0 0 0 0
## S.P.metropolitan.diary.colon A.T.appear A.T.archiv A.T.diari
## 87 0 0 0 0
## 157 0 0 0 0
## A.T.herald A.T.obama A.T.photo A.T.senat A.T.tribun A.T.word A.P.http
## 87 0 0 0 0 0 0 0
## 157 0 0 0 0 0 0 0
## A.P.year.colon A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 87 0 0 0 0
## 157 0 0 0 0
## A.P.metropolitan.diary.colon
## 87 0
## 157 0
## [1] "min distance(-0.0000) pair:"
## UniqueID Popular myCategory
## 1477 1477 0 Styles#U.S.#
## 6409 6409 0 Styles#U.S.#
## Headline H.T.X2014 H.T.X2015
## 1477 From Lymphoma Mom to Just Mom, and Holding 0 0
## 6409 Intensive Thanksgiving 0 0
## H.T.art H.T.bank H.T.big H.T.billion H.T.busi H.T.china H.T.daili
## 1477 0 0 0 0 0 0 0
## 6409 0 0 0 0 0 0 0
## H.T.day H.T.deal H.T.fashion H.T.first H.T.make H.T.morn H.T.new
## 1477 0 0 0 0 0 0 0
## 6409 0 0 0 0 0 0 0
## H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit H.T.report
## 1477 0 0 0 0 0 0
## 6409 0 0 0 0 0 0
## H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week
## 1477 0 0 0 0 0 0
## 6409 0 0 0 0 0 0
## S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first
## 1477 0 0 0 0 0 0
## 6409 0 0 0 0 0 0
## S.T.intern S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one
## 1477 0 0 0 0 0 0.3185063
## 6409 0 0 0 0 0 0.2346889
## S.T.presid S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time
## 1477 0 0 0 0 0 0 0
## 6409 0 0 0 0 0 0 0
## S.T.week S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day
## 1477 0 0 0 0 0 0 0
## 6409 0 0 0 0 0 0 0
## A.T.fashion A.T.first A.T.intern A.T.make A.T.new A.T.newyork
## 1477 0 0 0 0 0 0
## 6409 0 0 0 0 0 0
## A.T.newyorktim A.T.one A.T.presid A.T.report A.T.said A.T.share
## 1477 0 0.3182369 0 0 0 0
## 6409 0 0.2344903 0 0 0 0
## A.T.show A.T.take A.T.time A.T.week A.T.will A.T.year H.T.clip
## 1477 0 0 0 0 0 0 0
## 6409 0 0 0 0 0 0 0
## H.T.ebola H.T.get H.T.newyorktim H.T.read H.T.word H.P.http
## 1477 0 0 0 0 0 0
## 6409 0 0 0 0 0 0
## H.P.year.colon H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 1477 0 0 0 0
## 6409 0 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 1477 0 0 0
## 6409 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 1477 0 0 0 0
## 6409 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 1477 0 0 0
## 6409 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 1477 0 0 0 0 0
## 6409 0 0 0 0 0
## S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word S.P.http
## 1477 0 0 0 0 0 0 0
## 6409 0 0 0 0 0 0 0
## S.P.year.colon S.P.daily.clip.report S.P.fashion.week S.P.first.draft
## 1477 0 0 0 0
## 6409 0 0 0 0
## S.P.metropolitan.diary.colon A.T.appear A.T.archiv A.T.diari
## 1477 0 0 0 0
## 6409 0 0 0 0
## A.T.herald A.T.obama A.T.photo A.T.senat A.T.tribun A.T.word A.P.http
## 1477 0 0 0 0 0 0 0
## 6409 0 0 0 0 0 0 0
## A.P.year.colon A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 1477 0 0 0 0
## 6409 0 0 0 0
## A.P.metropolitan.diary.colon
## 1477 0
## 6409 0
## [1] "max distance(1.0000) pair:"
## UniqueID Popular myCategory
## 26 26 0 Business#Technology#
## 104 104 1 Business#Technology#
## Headline
## 26 Daily Report: Start-Ups Stockpile Funding for Harder Times to Come
## 104 German Court Bans Uber Service Nationwide
## H.T.X2014 H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi
## 26 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0
## H.T.china H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make
## 26 0 0.6602037 0 0 0 0 0
## 104 0 0.0000000 0 0 0 0 0
## H.T.morn H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit
## 26 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0
## H.T.report H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week
## 26 0.6166537 0 0 0 0 0 0
## 104 0.0000000 0 0 0 0 0 0
## S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first
## 26 0 0 0 0 0 0
## 104 0 0 0 0 0 0
## S.T.intern S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one
## 26 0 0 0 0 0 0
## 104 0 0 0 0 0 0
## S.T.presid S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time
## 26 0 0 0.000000 0 0 0.3317214 0.3244395
## 104 0 0 0.270945 0 0 0.0000000 0.0000000
## S.T.week S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day
## 26 0 0.0000000 0.0000000 0 0 0 0
## 104 0 0.2066444 0.2259859 0 0 0 0
## A.T.fashion A.T.first A.T.intern A.T.make A.T.new A.T.newyork
## 26 0 0 0 0 0 0
## 104 0 0 0 0 0 0
## A.T.newyorktim A.T.one A.T.presid A.T.report A.T.said A.T.share
## 26 0 0 0 0 0.000000 0
## 104 0 0 0 0 0.270945 0
## A.T.show A.T.take A.T.time A.T.week A.T.will A.T.year H.T.clip
## 26 0 0.3313619 0.3241061 0 0.0000000 0.0000000 0
## 104 0 0.0000000 0.0000000 0 0.2064141 0.2259859 0
## H.T.ebola H.T.get H.T.newyorktim H.T.read H.T.word H.P.http
## 26 0 0 0 0 0 0
## 104 0 0 0 0 0 0
## H.P.year.colon H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 26 0 0 0 0
## 104 0 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 26 0 0 0
## 104 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 26 0 0 0 0
## 104 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 26 0 0 0
## 104 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 26 0 0 0 0 0
## 104 0 0 0 0 0
## S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word S.P.http
## 26 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0
## S.P.year.colon S.P.daily.clip.report S.P.fashion.week S.P.first.draft
## 26 0 0 0 0
## 104 0 0 0 0
## S.P.metropolitan.diary.colon A.T.appear A.T.archiv A.T.diari
## 26 0 0 0 0
## 104 0 0 0 0
## A.T.herald A.T.obama A.T.photo A.T.senat A.T.tribun A.T.word A.P.http
## 26 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0
## A.P.year.colon A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 26 0 0 0 0
## 104 0 0 0 0
## A.P.metropolitan.diary.colon
## 26 0
## 104 0
## [1] "min distance(-0.0000) pair:"
## UniqueID Popular myCategory
## 284 284 0 Business#Technology#
## 3397 3397 0 Business#Technology#
## Headline H.T.X2014 H.T.X2015
## 284 Cisco Has a Server-Centric Vision for Itself 0 0
## 3397 Reddit Debuts an Official Mobile App 0 0
## H.T.art H.T.bank H.T.big H.T.billion H.T.busi H.T.china H.T.daili
## 284 0 0 0 0 0 0 0
## 3397 0 0 0 0 0 0 0
## H.T.day H.T.deal H.T.fashion H.T.first H.T.make H.T.morn H.T.new
## 284 0 0 0 0 0 0 0
## 3397 0 0 0 0 0 0 0
## H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit H.T.report
## 284 0 0 0 0 0 0
## 3397 0 0 0 0 0 0
## H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week
## 284 0 0 0 0 0 0
## 3397 0 0 0 0 0 0
## S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first
## 284 0 0 0 0 0 0
## 3397 0 0 0 0 0 0
## S.T.intern S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one
## 284 0 0 0 0 0 0
## 3397 0 0 0 0 0 0
## S.T.presid S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time
## 284 0 0 0 0 0 0 0
## 3397 0 0 0 0 0 0 0
## S.T.week S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day
## 284 0 0 0.2561173 0 0 0 0
## 3397 0 0 0.2955200 0 0 0 0
## A.T.fashion A.T.first A.T.intern A.T.make A.T.new A.T.newyork
## 284 0 0 0 0 0 0
## 3397 0 0 0 0 0 0
## A.T.newyorktim A.T.one A.T.presid A.T.report A.T.said A.T.share
## 284 0 0 0 0 0 0
## 3397 0 0 0 0 0 0
## A.T.show A.T.take A.T.time A.T.week A.T.will A.T.year H.T.clip
## 284 0 0 0 0 0 0.2561173 0
## 3397 0 0 0 0 0 0.2955200 0
## H.T.ebola H.T.get H.T.newyorktim H.T.read H.T.word H.P.http
## 284 0 0 0 0 0 0
## 3397 0 0 0 0 0 0
## H.P.year.colon H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 284 0 0 0 0
## 3397 0 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 284 0 0 0
## 3397 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 284 0 0 0 0
## 3397 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 284 0 0 0
## 3397 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 284 0 0 0 0 0
## 3397 0 0 0 0 0
## S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word S.P.http
## 284 0 0 0 0 0 0 0
## 3397 0 0 0 0 0 0 0
## S.P.year.colon S.P.daily.clip.report S.P.fashion.week S.P.first.draft
## 284 0 0 0 0
## 3397 0 0 0 0
## S.P.metropolitan.diary.colon A.T.appear A.T.archiv A.T.diari
## 284 0 0 0 0
## 3397 0 0 0 0
## A.T.herald A.T.obama A.T.photo A.T.senat A.T.tribun A.T.word A.P.http
## 284 0 0 0 0 0 0 0
## 3397 0 0 0 0 0 0 0
## A.P.year.colon A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 284 0 0 0 0
## 3397 0 0 0 0
## A.P.metropolitan.diary.colon
## 284 0
## 3397 0
## [1] "max distance(1.0000) pair:"
## UniqueID Popular myCategory Headline
## 33 33 1 Science#Health# Don't Catch What Ails Your House
## 7 7 0 Science#Health# Birth Weight and Diabetes
## H.T.X2014 H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi
## 33 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## H.T.china H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make
## 33 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## H.T.morn H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit
## 33 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## H.T.report H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week
## 33 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first S.T.intern
## 33 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one S.T.presid
## 33 0 0.0000000 0 0 0 0
## 7 0 0.2513463 0 0 0 0
## S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time S.T.week
## 33 0 0 0 0 0.3109888 0 0
## 7 0 0 0 0 0.0000000 0 0
## S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day A.T.fashion
## 33 0.2195597 0 0 0 0 0 0
## 7 0.0000000 0 0 0 0 0 0
## A.T.first A.T.intern A.T.make A.T.new A.T.newyork A.T.newyorktim
## 33 0 0 0 0.0000000 0 0
## 7 0 0 0 0.2512058 0 0
## A.T.one A.T.presid A.T.report A.T.said A.T.share A.T.show A.T.take
## 33 0 0 0 0 0 0 0.3106517
## 7 0 0 0 0 0 0 0.0000000
## A.T.time A.T.week A.T.will A.T.year H.T.clip H.T.ebola H.T.get
## 33 0 0 0.219315 0 0 0 0
## 7 0 0 0.000000 0 0 0 0
## H.T.newyorktim H.T.read H.T.word H.P.http H.P.year.colon
## 33 0 0 0 0 0
## 7 0 0 0 0 0
## H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 33 0 0 0
## 7 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 33 0 0 0
## 7 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 33 0 0 0 0
## 7 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 33 0 0 0
## 7 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 33 0 0 0 0 0
## 7 0 0 0 0 0
## S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word S.P.http
## 33 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0
## S.P.year.colon S.P.daily.clip.report S.P.fashion.week S.P.first.draft
## 33 0 0 0 0
## 7 0 0 0 0
## S.P.metropolitan.diary.colon A.T.appear A.T.archiv A.T.diari A.T.herald
## 33 0 0 0 0 0
## 7 0 0 0 0 0
## A.T.obama A.T.photo A.T.senat A.T.tribun A.T.word A.P.http
## 33 0 0 0 0 0 0
## 7 0 0 0 0 0 0
## A.P.year.colon A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 33 0 0 0 0
## 7 0 0 0 0
## A.P.metropolitan.diary.colon
## 33 0
## 7 0
## [1] "min distance(-0.0000) pair:"
## UniqueID Popular myCategory Headline
## 1927 1927 1 Science#Health# My Father's A.L.S.
## 4619 4619 1 Science#Health# Pronouncing the Patient Dead
## H.T.X2014 H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi
## 1927 0 0 0 0 0 0 0
## 4619 0 0 0 0 0 0 0
## H.T.china H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make
## 1927 0 0 0 0 0 0 0
## 4619 0 0 0 0 0 0 0
## H.T.morn H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit
## 1927 0 0 0 0 0 0 0
## 4619 0 0 0 0 0 0 0
## H.T.report H.T.say H.T.springsumm H.T.take H.T.test H.T.today
## 1927 0 0 0 0 0 0
## 4619 0 0 0 0 0 0
## H.T.week S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first
## 1927 0 0 0 0 0 0 0
## 4619 0 0 0 0 0 0 0
## S.T.intern S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one
## 1927 0 0 0 0 0 0.3185063
## 4619 0 0 0 0 0 0.2346889
## S.T.presid S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time
## 1927 0 0 0 0 0 0 0
## 4619 0 0 0 0 0 0 0
## S.T.week S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day
## 1927 0 0 0 0 0 0 0
## 4619 0 0 0 0 0 0 0
## A.T.fashion A.T.first A.T.intern A.T.make A.T.new A.T.newyork
## 1927 0 0 0 0 0 0
## 4619 0 0 0 0 0 0
## A.T.newyorktim A.T.one A.T.presid A.T.report A.T.said A.T.share
## 1927 0 0.3182369 0 0 0 0
## 4619 0 0.2344903 0 0 0 0
## A.T.show A.T.take A.T.time A.T.week A.T.will A.T.year H.T.clip
## 1927 0 0 0 0 0 0 0
## 4619 0 0 0 0 0 0 0
## H.T.ebola H.T.get H.T.newyorktim H.T.read H.T.word H.P.http
## 1927 0 0 0 0 0 0
## 4619 0 0 0 0 0 0
## H.P.year.colon H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 1927 0 0 0 0
## 4619 0 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 1927 0 0 0
## 4619 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 1927 0 0 0 0
## 4619 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 1927 0 0 0
## 4619 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 1927 0 0 0 0 0
## 4619 0 0 0 0 0
## S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word S.P.http
## 1927 0 0 0 0 0 0 0
## 4619 0 0 0 0 0 0 0
## S.P.year.colon S.P.daily.clip.report S.P.fashion.week S.P.first.draft
## 1927 0 0 0 0
## 4619 0 0 0 0
## S.P.metropolitan.diary.colon A.T.appear A.T.archiv A.T.diari
## 1927 0 0 0 0
## 4619 0 0 0 0
## A.T.herald A.T.obama A.T.photo A.T.senat A.T.tribun A.T.word A.P.http
## 1927 0 0 0 0 0 0 0
## 4619 0 0 0 0 0 0 0
## A.P.year.colon A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 1927 0 0 0 0
## 4619 0 0 0 0
## A.P.metropolitan.diary.colon
## 1927 0
## 4619 0
## [1] "max distance(1.0000) pair:"
## UniqueID Popular myCategory Headline
## 30 30 0 Culture#Arts# Fabio Luisi Has a New Gig
## 13 13 0 Culture#Arts# Kate Bush Bombards British Album Chart
## H.T.X2014 H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi
## 30 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0
## H.T.china H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make
## 30 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0
## H.T.morn H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit
## 30 0 1.201925 0 0 0 0 0
## 13 0 0.000000 0 0 0 0 0
## H.T.report H.T.say H.T.springsumm H.T.take H.T.test H.T.today H.T.week
## 30 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0
## S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first S.T.intern
## 30 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0
## S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one S.T.presid
## 30 0 0 0 0 0 0
## 13 0 0 0 0 0 0
## S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time S.T.week
## 30 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0
## S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day A.T.fashion
## 30 0.2195597 0 0 0 0 0 0
## 13 0.0000000 0 0 0 0 0 0
## A.T.first A.T.intern A.T.make A.T.new A.T.newyork A.T.newyorktim
## 30 0 0 0 0 0 0
## 13 0 0 0 0 0 0
## A.T.one A.T.presid A.T.report A.T.said A.T.share A.T.show A.T.take
## 30 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0
## A.T.time A.T.week A.T.will A.T.year H.T.clip H.T.ebola H.T.get
## 30 0 0 0.219315 0 0 0 0
## 13 0 0 0.000000 0 0 0 0
## H.T.newyorktim H.T.read H.T.word H.P.http H.P.year.colon
## 30 0 0 0 0 0
## 13 0 0 0 0 0
## H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 30 0 0 0
## 13 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 30 0 0 0
## 13 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 30 0 0 0 0
## 13 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 30 0 0 0
## 13 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 30 0 0 0 0 0
## 13 0 0 0 0 0
## S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word S.P.http
## 30 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0
## S.P.year.colon S.P.daily.clip.report S.P.fashion.week S.P.first.draft
## 30 0 0 0 0
## 13 0 0 0 0
## S.P.metropolitan.diary.colon A.T.appear A.T.archiv A.T.diari A.T.herald
## 30 0 0 0 0 0
## 13 0 0 0 0 0
## A.T.obama A.T.photo A.T.senat A.T.tribun A.T.word A.P.http
## 30 0 0 0 0 0 0
## 13 0 0 0 0 0 0
## A.P.year.colon A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 30 0 0 0 0
## 13 0 0 0 0
## A.P.metropolitan.diary.colon
## 30 0
## 13 0
## [1] "min distance(-0.0000) pair:"
## UniqueID Popular myCategory
## 1101 1101 0 Culture#Arts#
## 3618 3618 0 Culture#Arts#
## Headline
## 1101 Stratford Festival to Offer Shakespeare in Movie Theaters and on TV
## 3618 Cabaret Cram Session Aims at a World Record
## H.T.X2014 H.T.X2015 H.T.art H.T.bank H.T.big H.T.billion H.T.busi
## 1101 0 0 0 0 0 0 0
## 3618 0 0 0 0 0 0 0
## H.T.china H.T.daili H.T.day H.T.deal H.T.fashion H.T.first H.T.make
## 1101 0 0 0 0 0 0 0
## 3618 0 0 0 0 0 0 0
## H.T.morn H.T.new H.T.news H.T.newyork H.T.obama H.T.pictur H.T.polit
## 1101 0 0 0 0 0 0 0
## 3618 0 0 0 0 0 0 0
## H.T.report H.T.say H.T.springsumm H.T.take H.T.test H.T.today
## 1101 0 0 0 0 0 0
## 3618 0 0 0 0 0 0
## H.T.week S.T.articl S.T.can S.T.compani S.T.day S.T.fashion S.T.first
## 1101 0 0 0 0 0 0 0
## 3618 0 0 0 0 0 0 0
## S.T.intern S.T.make S.T.new S.T.newyork S.T.newyorktim S.T.one
## 1101 0 0 0 0 0 0
## 3618 0 0 0 0 0 0
## S.T.presid S.T.report S.T.said S.T.share S.T.show S.T.take S.T.time
## 1101 0 0 0 0 0.3565802 0 0
## 3618 0 0 0 0 0.3840095 0 0
## S.T.week S.T.will S.T.year A.T.articl A.T.can A.T.compani A.T.day
## 1101 0 0.2509254 0 0 0 0 0
## 3618 0 0.2702273 0 0 0 0 0
## A.T.fashion A.T.first A.T.intern A.T.make A.T.new A.T.newyork
## 1101 0 0 0 0 0 0
## 3618 0 0 0 0 0 0
## A.T.newyorktim A.T.one A.T.presid A.T.report A.T.said A.T.share
## 1101 0 0 0 0 0 0
## 3618 0 0 0 0 0 0
## A.T.show A.T.take A.T.time A.T.week A.T.will A.T.year H.T.clip
## 1101 0.3565802 0 0 0 0.2506457 0 0
## 3618 0.3840095 0 0 0 0.2699262 0 0
## H.T.ebola H.T.get H.T.newyorktim H.T.read H.T.word H.P.http
## 1101 0 0 0 0 0 0
## 3618 0 0 0 0 0 0
## H.P.year.colon H.P.daily.clip.report H.P.fashion.week H.P.first.draft
## 1101 0 0 0 0
## 3618 0 0 0 0
## H.P.facts.figures H.P.friday.night.music H.P.no.comment.colon
## 1101 0 0 0
## 3618 0 0 0
## H.P.on.this.day H.P.quandary H.P.readers.respond H.P.recap.colon
## 1101 0 0 0 0
## 3618 0 0 0 0
## H.P.s.notebook H.P.today.in.politic H.P.today.in.smallbusiness
## 1101 0 0 0
## 3618 0 0 0
## H.P.verbatim.colon H.P.what.we.are S.T.appear S.T.archiv S.T.diari
## 1101 0 0 0 0 0
## 3618 0 0 0 0 0
## S.T.herald S.T.obama S.T.photo S.T.senat S.T.tribun S.T.word S.P.http
## 1101 0 0 0 0 0 0 0
## 3618 0 0 0 0 0 0 0
## S.P.year.colon S.P.daily.clip.report S.P.fashion.week S.P.first.draft
## 1101 0 0 0 0
## 3618 0 0 0 0
## S.P.metropolitan.diary.colon A.T.appear A.T.archiv A.T.diari
## 1101 0 0 0 0
## 3618 0 0 0 0
## A.T.herald A.T.obama A.T.photo A.T.senat A.T.tribun A.T.word A.P.http
## 1101 0 0 0 0 0 0 0
## 3618 0 0 0 0 0 0 0
## A.P.year.colon A.P.daily.clip.report A.P.fashion.week A.P.first.draft
## 1101 0 0 0 0
## 3618 0 0 0 0
## A.P.metropolitan.diary.colon
## 1101 0
## 3618 0
## myCategory .clusterid N Y NA
## 69 OpEd#Opinion# 1 51 247 94
## 42 Business#Crosswords/Games# 1 20 103 42
## 77 Science#Health# 1 28 66 37
## 82 Styles#U.S.# 1 22 41 30
## 1 ## 1 204 39 50
## 70 OpEd#Opinion# 2 16 36 22
## 71 OpEd#Opinion# 3 19 36 14
## 54 Culture#Arts# 4 31 33 8
## 25 Business#Business Day#Dealbook 1 239 27 63
## 83 Styles#U.S.# 2 23 27 14
## 72 OpEd#Opinion# 4 6 27 11
## 73 OpEd#Opinion# 5 8 21 4
## 84 Styles#U.S.# 3 14 20 11
## 80 Science#Health# 4 16 20 3
## 79 Science#Health# 3 15 19 7
## 74 OpEd#Opinion# 6 6 19 5
## 67 Metro#N.Y. / Region# 1 181 17 67
## 78 Science#Health# 2 15 17 10
## 23 #Opinion#The Public Editor 1 4 16 10
## 75 OpEd#Opinion# 7 2 14 8
## 46 Business#Technology# 4 36 12 14
## 85 Styles#U.S.# 4 18 12 7
## 45 Business#Technology# 3 45 11 21
## 44 Business#Technology# 2 49 11 19
## 87 TStyle## 1 715 9 105
## 26 Business#Business Day#Dealbook 2 66 9 30
## 3 ## 3 85 9 28
## 9 ## 9 47 9 20
## 11 ## 11 46 9 11
## 76 OpEd#Opinion# 8 7 8 6
## 27 Business#Business Day#Dealbook 3 60 7 34
## 22 #Opinion#Room For Debate 1 69 7 24
## 29 Business#Business Day#Dealbook 5 50 7 22
## 8 ## 8 55 7 14
## 34 Business#Business Day#Dealbook 10 34 7 12
## 15 ## 15 41 7 8
## 51 Culture#Arts# 1 145 6 71
## 43 Business#Technology# 1 62 6 25
## 6 ## 6 56 6 19
## 49 Business#Technology# 7 23 6 8
## 41 Business#Business Day#Small Business 1 135 5 42
## 31 Business#Business Day#Dealbook 7 47 5 19
## 32 Business#Business Day#Dealbook 8 40 5 15
## 13 ## 13 50 5 5
## 17 ## 17 35 5 2
## 52 Culture#Arts# 2 95 4 31
## 5 ## 5 62 4 18
## 28 Business#Business Day#Dealbook 4 59 4 16
## 37 Business#Business Day#Dealbook 13 28 4 13
## 47 Business#Technology# 5 24 4 10
## 66 Foreign#World#Asia Pacific 1 200 3 56
## 30 Business#Business Day#Dealbook 6 54 3 15
## 39 Business#Business Day#Dealbook 15 20 3 14
## 36 Business#Business Day#Dealbook 12 32 3 12
## 21 #Multimedia# 1 139 2 52
## 7 ## 7 55 2 23
## 55 Culture#Arts# 5 50 2 17
## 12 ## 12 47 2 15
## 35 Business#Business Day#Dealbook 11 35 2 12
## 2 ## 2 103 1 46
## 86 Travel#Travel# 1 116 1 35
## 57 Culture#Arts# 7 35 1 24
## 58 Culture#Arts# 8 39 1 19
## 81 Styles##Fashion 1 118 1 15
## 18 ## 18 26 1 14
## 33 Business#Business Day#Dealbook 9 39 1 14
## 62 Culture#Arts# 12 15 1 11
## 16 ## 16 44 1 10
## 19 ## 19 14 1 10
## 40 Business#Business Day#Dealbook 16 26 1 8
## 50 Business#Technology# 8 20 1 8
## 60 Culture#Arts# 10 19 1 7
## 63 Culture#Arts# 13 16 1 7
## 14 ## 14 51 1 6
## 20 ## 20 17 1 4
## 24 #U.S.#Education 1 325 0 90
## 65 Foreign#World# 1 172 0 47
## 4 ## 4 64 0 23
## 56 Culture#Arts# 6 47 0 20
## 53 Culture#Arts# 3 61 0 13
## 10 ## 10 61 0 12
## 61 Culture#Arts# 11 18 0 9
## 48 Business#Technology# 6 29 0 8
## 38 Business#Business Day#Dealbook 14 35 0 5
## 64 Culture#Arts# 14 18 0 4
## 59 Culture#Arts# 9 37 0 3
## 68 myOther 1 38 0 3
## , , = N
##
##
## 1 2 3 4 5 6 7 8 9
## ## 204 103 85 64 62 56 55 55 47
## #Multimedia# 139 0 0 0 0 0 0 0 0
## #Opinion#Room For Debate 69 0 0 0 0 0 0 0 0
## #Opinion#The Public Editor 4 0 0 0 0 0 0 0 0
## #U.S.#Education 325 0 0 0 0 0 0 0 0
## Business#Business Day#Dealbook 239 66 60 59 50 54 47 40 39
## Business#Business Day#Small Business 135 0 0 0 0 0 0 0 0
## Business#Crosswords/Games# 20 0 0 0 0 0 0 0 0
## Business#Technology# 62 49 45 36 24 29 23 20 0
## Culture#Arts# 145 95 61 31 50 47 35 39 37
## Foreign#World# 172 0 0 0 0 0 0 0 0
## Foreign#World#Asia Pacific 200 0 0 0 0 0 0 0 0
## Metro#N.Y. / Region# 181 0 0 0 0 0 0 0 0
## myOther 38 0 0 0 0 0 0 0 0
## OpEd#Opinion# 51 16 19 6 8 6 2 7 0
## Science#Health# 28 15 15 16 0 0 0 0 0
## Styles##Fashion 118 0 0 0 0 0 0 0 0
## Styles#U.S.# 22 23 14 18 0 0 0 0 0
## Travel#Travel# 116 0 0 0 0 0 0 0 0
## TStyle## 715 0 0 0 0 0 0 0 0
##
## 10 11 12 13 14 15 16 17 18
## ## 61 46 47 50 51 41 44 35 26
## #Multimedia# 0 0 0 0 0 0 0 0 0
## #Opinion#Room For Debate 0 0 0 0 0 0 0 0 0
## #Opinion#The Public Editor 0 0 0 0 0 0 0 0 0
## #U.S.#Education 0 0 0 0 0 0 0 0 0
## Business#Business Day#Dealbook 34 35 32 28 35 20 26 0 0
## Business#Business Day#Small Business 0 0 0 0 0 0 0 0 0
## Business#Crosswords/Games# 0 0 0 0 0 0 0 0 0
## Business#Technology# 0 0 0 0 0 0 0 0 0
## Culture#Arts# 19 18 15 16 18 0 0 0 0
## Foreign#World# 0 0 0 0 0 0 0 0 0
## Foreign#World#Asia Pacific 0 0 0 0 0 0 0 0 0
## Metro#N.Y. / Region# 0 0 0 0 0 0 0 0 0
## myOther 0 0 0 0 0 0 0 0 0
## OpEd#Opinion# 0 0 0 0 0 0 0 0 0
## Science#Health# 0 0 0 0 0 0 0 0 0
## Styles##Fashion 0 0 0 0 0 0 0 0 0
## Styles#U.S.# 0 0 0 0 0 0 0 0 0
## Travel#Travel# 0 0 0 0 0 0 0 0 0
## TStyle## 0 0 0 0 0 0 0 0 0
##
## 19 20
## ## 14 17
## #Multimedia# 0 0
## #Opinion#Room For Debate 0 0
## #Opinion#The Public Editor 0 0
## #U.S.#Education 0 0
## Business#Business Day#Dealbook 0 0
## Business#Business Day#Small Business 0 0
## Business#Crosswords/Games# 0 0
## Business#Technology# 0 0
## Culture#Arts# 0 0
## Foreign#World# 0 0
## Foreign#World#Asia Pacific 0 0
## Metro#N.Y. / Region# 0 0
## myOther 0 0
## OpEd#Opinion# 0 0
## Science#Health# 0 0
## Styles##Fashion 0 0
## Styles#U.S.# 0 0
## Travel#Travel# 0 0
## TStyle## 0 0
##
## , , = Y
##
##
## 1 2 3 4 5 6 7 8 9
## ## 39 1 9 0 4 6 2 7 9
## #Multimedia# 2 0 0 0 0 0 0 0 0
## #Opinion#Room For Debate 7 0 0 0 0 0 0 0 0
## #Opinion#The Public Editor 16 0 0 0 0 0 0 0 0
## #U.S.#Education 0 0 0 0 0 0 0 0 0
## Business#Business Day#Dealbook 27 9 7 4 7 3 5 5 1
## Business#Business Day#Small Business 5 0 0 0 0 0 0 0 0
## Business#Crosswords/Games# 103 0 0 0 0 0 0 0 0
## Business#Technology# 6 11 11 12 4 0 6 1 0
## Culture#Arts# 6 4 0 33 2 0 1 1 0
## Foreign#World# 0 0 0 0 0 0 0 0 0
## Foreign#World#Asia Pacific 3 0 0 0 0 0 0 0 0
## Metro#N.Y. / Region# 17 0 0 0 0 0 0 0 0
## myOther 0 0 0 0 0 0 0 0 0
## OpEd#Opinion# 247 36 36 27 21 19 14 8 0
## Science#Health# 66 17 19 20 0 0 0 0 0
## Styles##Fashion 1 0 0 0 0 0 0 0 0
## Styles#U.S.# 41 27 20 12 0 0 0 0 0
## Travel#Travel# 1 0 0 0 0 0 0 0 0
## TStyle## 9 0 0 0 0 0 0 0 0
##
## 10 11 12 13 14 15 16 17 18
## ## 0 9 2 5 1 7 1 5 1
## #Multimedia# 0 0 0 0 0 0 0 0 0
## #Opinion#Room For Debate 0 0 0 0 0 0 0 0 0
## #Opinion#The Public Editor 0 0 0 0 0 0 0 0 0
## #U.S.#Education 0 0 0 0 0 0 0 0 0
## Business#Business Day#Dealbook 7 2 3 4 0 3 1 0 0
## Business#Business Day#Small Business 0 0 0 0 0 0 0 0 0
## Business#Crosswords/Games# 0 0 0 0 0 0 0 0 0
## Business#Technology# 0 0 0 0 0 0 0 0 0
## Culture#Arts# 1 0 1 1 0 0 0 0 0
## Foreign#World# 0 0 0 0 0 0 0 0 0
## Foreign#World#Asia Pacific 0 0 0 0 0 0 0 0 0
## Metro#N.Y. / Region# 0 0 0 0 0 0 0 0 0
## myOther 0 0 0 0 0 0 0 0 0
## OpEd#Opinion# 0 0 0 0 0 0 0 0 0
## Science#Health# 0 0 0 0 0 0 0 0 0
## Styles##Fashion 0 0 0 0 0 0 0 0 0
## Styles#U.S.# 0 0 0 0 0 0 0 0 0
## Travel#Travel# 0 0 0 0 0 0 0 0 0
## TStyle## 0 0 0 0 0 0 0 0 0
##
## 19 20
## ## 1 1
## #Multimedia# 0 0
## #Opinion#Room For Debate 0 0
## #Opinion#The Public Editor 0 0
## #U.S.#Education 0 0
## Business#Business Day#Dealbook 0 0
## Business#Business Day#Small Business 0 0
## Business#Crosswords/Games# 0 0
## Business#Technology# 0 0
## Culture#Arts# 0 0
## Foreign#World# 0 0
## Foreign#World#Asia Pacific 0 0
## Metro#N.Y. / Region# 0 0
## myOther 0 0
## OpEd#Opinion# 0 0
## Science#Health# 0 0
## Styles##Fashion 0 0
## Styles#U.S.# 0 0
## Travel#Travel# 0 0
## TStyle## 0 0
##
## , , = NA
##
##
## 1 2 3 4 5 6 7 8 9
## ## 50 46 28 23 18 19 23 14 20
## #Multimedia# 52 0 0 0 0 0 0 0 0
## #Opinion#Room For Debate 24 0 0 0 0 0 0 0 0
## #Opinion#The Public Editor 10 0 0 0 0 0 0 0 0
## #U.S.#Education 90 0 0 0 0 0 0 0 0
## Business#Business Day#Dealbook 63 30 34 16 22 15 19 15 14
## Business#Business Day#Small Business 42 0 0 0 0 0 0 0 0
## Business#Crosswords/Games# 42 0 0 0 0 0 0 0 0
## Business#Technology# 25 19 21 14 10 8 8 8 0
## Culture#Arts# 71 31 13 8 17 20 24 19 3
## Foreign#World# 47 0 0 0 0 0 0 0 0
## Foreign#World#Asia Pacific 56 0 0 0 0 0 0 0 0
## Metro#N.Y. / Region# 67 0 0 0 0 0 0 0 0
## myOther 3 0 0 0 0 0 0 0 0
## OpEd#Opinion# 94 22 14 11 4 5 8 6 0
## Science#Health# 37 10 7 3 0 0 0 0 0
## Styles##Fashion 15 0 0 0 0 0 0 0 0
## Styles#U.S.# 30 14 11 7 0 0 0 0 0
## Travel#Travel# 35 0 0 0 0 0 0 0 0
## TStyle## 105 0 0 0 0 0 0 0 0
##
## 10 11 12 13 14 15 16 17 18
## ## 12 11 15 5 6 8 10 2 14
## #Multimedia# 0 0 0 0 0 0 0 0 0
## #Opinion#Room For Debate 0 0 0 0 0 0 0 0 0
## #Opinion#The Public Editor 0 0 0 0 0 0 0 0 0
## #U.S.#Education 0 0 0 0 0 0 0 0 0
## Business#Business Day#Dealbook 12 12 12 13 5 14 8 0 0
## Business#Business Day#Small Business 0 0 0 0 0 0 0 0 0
## Business#Crosswords/Games# 0 0 0 0 0 0 0 0 0
## Business#Technology# 0 0 0 0 0 0 0 0 0
## Culture#Arts# 7 9 11 7 4 0 0 0 0
## Foreign#World# 0 0 0 0 0 0 0 0 0
## Foreign#World#Asia Pacific 0 0 0 0 0 0 0 0 0
## Metro#N.Y. / Region# 0 0 0 0 0 0 0 0 0
## myOther 0 0 0 0 0 0 0 0 0
## OpEd#Opinion# 0 0 0 0 0 0 0 0 0
## Science#Health# 0 0 0 0 0 0 0 0 0
## Styles##Fashion 0 0 0 0 0 0 0 0 0
## Styles#U.S.# 0 0 0 0 0 0 0 0 0
## Travel#Travel# 0 0 0 0 0 0 0 0 0
## TStyle## 0 0 0 0 0 0 0 0 0
##
## 19 20
## ## 10 4
## #Multimedia# 0 0
## #Opinion#Room For Debate 0 0
## #Opinion#The Public Editor 0 0
## #U.S.#Education 0 0
## Business#Business Day#Dealbook 0 0
## Business#Business Day#Small Business 0 0
## Business#Crosswords/Games# 0 0
## Business#Technology# 0 0
## Culture#Arts# 0 0
## Foreign#World# 0 0
## Foreign#World#Asia Pacific 0 0
## Metro#N.Y. / Region# 0 0
## myOther 0 0
## OpEd#Opinion# 0 0
## Science#Health# 0 0
## Styles##Fashion 0 0
## Styles#U.S.# 0 0
## Travel#Travel# 0 0
## TStyle## 0 0# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 7 cluster.data 4 0 262.194 278.878 16.684
## 8 select.features 5 0 278.879 NA NAStep 5.0: select features
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## id cor.y
## Popular Popular 1.000000e+00
## A.nuppr.log A.nuppr.log -2.720962e-01
## S.nuppr.log S.nuppr.log -2.718459e-01
## WordCount.log WordCount.log 2.649526e-01
## A.ratio.sum.TfIdf.nwrds A.ratio.sum.TfIdf.nwrds 2.623865e-01
## S.ratio.sum.TfIdf.nwrds S.ratio.sum.TfIdf.nwrds 2.622549e-01
## WordCount WordCount 2.575265e-01
## S.nwrds.unq.log S.nwrds.unq.log -2.461670e-01
## A.nwrds.unq.log A.nwrds.unq.log -2.460117e-01
## H.ratio.sum.TfIdf.nwrds H.ratio.sum.TfIdf.nwrds 2.254527e-01
## S.nchrs.log S.nchrs.log -2.246930e-01
## A.nchrs.log A.nchrs.log -2.245488e-01
## H.nwrds.unq.log H.nwrds.unq.log -2.014127e-01
## A.nwrds.log A.nwrds.log -1.978712e-01
## S.nwrds.log S.nwrds.log -1.978341e-01
## H.nchrs.log H.nchrs.log -1.710624e-01
## myCategory.fctr myCategory.fctr 1.704362e-01
## H.nwrds.log H.nwrds.log -1.573431e-01
## PubDate.day.minutes.poly.1 PubDate.day.minutes.poly.1 1.567535e-01
## H.sum.TfIdf H.sum.TfIdf 1.520414e-01
## S.sum.TfIdf S.sum.TfIdf 1.484963e-01
## A.sum.TfIdf A.sum.TfIdf 1.478461e-01
## PubDate.hour.fctr PubDate.hour.fctr 1.354368e-01
## H.npnct19.log H.npnct19.log 1.283641e-01
## H.nuppr.log H.nuppr.log -1.278085e-01
## A.ndgts.log A.ndgts.log -1.249484e-01
## S.ndgts.log S.ndgts.log -1.242046e-01
## A.ratio.nstopwrds.nwrds A.ratio.nstopwrds.nwrds 1.213545e-01
## S.ratio.nstopwrds.nwrds S.ratio.nstopwrds.nwrds 1.206896e-01
## H.ndgts.log H.ndgts.log -1.196633e-01
## A.nstopwrds.log A.nstopwrds.log -1.153879e-01
## S.nstopwrds.log S.nstopwrds.log -1.148150e-01
## PubDate.wkend PubDate.wkend 1.067288e-01
## A.npnct11.log A.npnct11.log -9.183870e-02
## S.npnct11.log S.npnct11.log -9.158156e-02
## H.P.recap.colon H.P.recap.colon 9.008096e-02
## H.npnct28.log H.npnct28.log -8.917338e-02
## .clusterid .clusterid -8.874584e-02
## .clusterid.fctr .clusterid.fctr -8.874584e-02
## H.P.quandary H.P.quandary 8.734922e-02
## H.nstopwrds.log H.nstopwrds.log -8.657067e-02
## S.T.week S.T.week -8.552704e-02
## A.T.week A.T.week -8.542792e-02
## S.T.fashion S.T.fashion -8.417159e-02
## A.T.fashion A.T.fashion -8.416793e-02
## H.npnct15.log H.npnct15.log -8.273237e-02
## H.T.fashion H.T.fashion -7.947505e-02
## H.P.year.colon H.P.year.colon -7.842875e-02
## H.P.fashion.week H.P.fashion.week -7.632046e-02
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.4 7.394139e-02
## S.T.archiv S.T.archiv -7.202808e-02
## A.T.archiv A.T.archiv -7.202808e-02
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.2 7.097772e-02
## S.P.fashion.week S.P.fashion.week -7.080716e-02
## A.P.fashion.week A.P.fashion.week -7.080716e-02
## S.T.tribun S.T.tribun -7.013418e-02
## A.T.tribun A.T.tribun -7.013418e-02
## S.T.intern S.T.intern -6.956906e-02
## A.T.intern A.T.intern -6.953025e-02
## A.npnct15.log A.npnct15.log -6.893301e-02
## S.T.photo S.T.photo -6.874283e-02
## A.T.photo A.T.photo -6.873838e-02
## H.T.week H.T.week -6.827601e-02
## S.npnct15.log S.npnct15.log -6.770952e-02
## S.T.herald S.T.herald -6.752419e-02
## A.T.herald A.T.herald -6.752419e-02
## H.T.X2015 H.T.X2015 -6.601141e-02
## H.T.daili H.T.daili -6.303731e-02
## S.npnct04.log S.npnct04.log -6.294642e-02
## A.npnct04.log A.npnct04.log -6.294642e-02
## H.T.report H.T.report -6.238114e-02
## S.T.diari S.T.diari -6.229931e-02
## A.T.diari A.T.diari -6.229931e-02
## H.npnct14.log H.npnct14.log -6.158577e-02
## H.P.no.comment.colon H.P.no.comment.colon 6.074669e-02
## H.T.day H.T.day -6.044381e-02
## H.T.springsumm H.T.springsumm -5.943248e-02
## H.T.today H.T.today -5.833786e-02
## PubDate.day.minutes.poly.5 PubDate.day.minutes.poly.5 -5.592923e-02
## H.T.newyork H.T.newyork -5.564999e-02
## S.npnct19.log S.npnct19.log 5.503894e-02
## A.npnct19.log A.npnct19.log 5.482747e-02
## S.T.articl S.T.articl -5.471737e-02
## A.T.articl A.T.articl -5.470831e-02
## H.P.facts.figures H.P.facts.figures 5.410097e-02
## PubDate.last10 PubDate.last10 5.398093e-02
## H.npnct08.log H.npnct08.log 5.375262e-02
## S.npnct13.log S.npnct13.log -5.332519e-02
## H.npnct04.log H.npnct04.log -5.126277e-02
## S.T.share S.T.share -5.105597e-02
## A.T.share A.T.share -5.105597e-02
## A.npnct13.log A.npnct13.log -4.999563e-02
## S.T.newyorktim S.T.newyorktim -4.985328e-02
## A.T.newyorktim A.T.newyorktim -4.984782e-02
## PubDate.last10.log PubDate.last10.log 4.931702e-02
## H.T.busi H.T.busi -4.899819e-02
## H.T.morn H.T.morn -4.838380e-02
## S.T.word S.T.word -4.822452e-02
## A.T.word A.T.word -4.821561e-02
## S.T.compani S.T.compani -4.787994e-02
## S.T.report S.T.report -4.779877e-02
## A.T.compani A.T.compani -4.774812e-02
## A.T.report A.T.report -4.774593e-02
## S.T.newyork S.T.newyork -4.694998e-02
## A.T.newyork A.T.newyork -4.686921e-02
## PubDate.last1.log PubDate.last1.log 4.635751e-02
## H.T.X2014 H.T.X2014 -4.523858e-02
## H.T.first H.T.first -4.472902e-02
## S.T.first S.T.first -4.447317e-02
## H.T.news H.T.news -4.436368e-02
## A.T.first A.T.first -4.433630e-02
## H.P.readers.respond H.P.readers.respond 4.432886e-02
## H.T.clip H.T.clip -4.388279e-02
## H.P.daily.clip.report H.P.daily.clip.report -4.388279e-02
## S.P.daily.clip.report S.P.daily.clip.report -4.388279e-02
## A.P.daily.clip.report A.P.daily.clip.report -4.388279e-02
## A.npnct28.log A.npnct28.log -4.373349e-02
## S.npnct28.log S.npnct28.log -4.370037e-02
## H.P.first.draft H.P.first.draft -4.316253e-02
## A.T.day A.T.day -4.270831e-02
## S.T.day S.T.day -4.262213e-02
## H.P.today.in.smallbusiness H.P.today.in.smallbusiness -4.243051e-02
## A.T.show A.T.show -4.185292e-02
## S.T.show S.T.show -4.182920e-02
## S.T.senat S.T.senat -4.143422e-02
## A.T.senat A.T.senat -4.139980e-02
## A.T.make A.T.make 4.124187e-02
## S.T.make S.T.make 4.118050e-02
## H.T.new H.T.new -4.111696e-02
## H.ratio.nstopwrds.nwrds H.ratio.nstopwrds.nwrds 4.024406e-02
## H.T.pictur H.T.pictur -4.003882e-02
## PubDate.last100 PubDate.last100 3.989229e-02
## PubDate.wkday.fctr PubDate.wkday.fctr -3.980129e-02
## S.T.appear S.T.appear -3.941362e-02
## A.T.appear A.T.appear -3.941362e-02
## S.T.will S.T.will -3.888838e-02
## A.T.will A.T.will -3.884318e-02
## H.P.what.we.are H.P.what.we.are -3.775209e-02
## A.npnct12.log A.npnct12.log -3.760012e-02
## S.T.year S.T.year -3.756011e-02
## A.T.year A.T.year -3.741571e-02
## H.P.today.in.politic H.P.today.in.politic -3.733661e-02
## S.npnct12.log S.npnct12.log -3.638891e-02
## PubDate.last1 PubDate.last1 3.592267e-02
## H.T.read H.T.read -3.467043e-02
## PubDate.minute.fctr PubDate.minute.fctr -3.407385e-02
## H.T.get H.T.get 3.300192e-02
## H.T.art H.T.art -3.291486e-02
## H.T.china H.T.china -3.283653e-02
## H.P.verbatim.colon H.P.verbatim.colon -3.194363e-02
## H.npnct06.log H.npnct06.log 3.190718e-02
## S.npnct01.log S.npnct01.log 3.093101e-02
## A.npnct01.log A.npnct01.log 3.093101e-02
## A.T.can A.T.can 3.083389e-02
## H.T.polit H.T.polit -3.058564e-02
## H.npnct16.log H.npnct16.log 3.039622e-02
## S.T.can S.T.can 3.005998e-02
## S.P.metropolitan.diary.colon S.P.metropolitan.diary.colon -2.841404e-02
## A.P.metropolitan.diary.colon A.P.metropolitan.diary.colon -2.841404e-02
## PubDate.day.minutes.poly.3 PubDate.day.minutes.poly.3 2.798355e-02
## H.T.billion H.T.billion -2.776561e-02
## S.npnct21.log S.npnct21.log 2.760321e-02
## S.npnct23.log S.npnct23.log 2.760321e-02
## H.T.ebola H.T.ebola 2.682920e-02
## A.T.new A.T.new -2.597887e-02
## S.T.new S.T.new -2.592872e-02
## H.T.deal H.T.deal -2.556237e-02
## H.npnct13.log H.npnct13.log -2.524770e-02
## H.T.newyorktim H.T.newyorktim -2.514415e-02
## A.T.time A.T.time -2.430509e-02
## S.T.time S.T.time -2.416246e-02
## A.npnct14.log A.npnct14.log -2.407715e-02
## S.npnct06.log S.npnct06.log -2.389145e-02
## A.npnct06.log A.npnct06.log -2.389145e-02
## A.T.take A.T.take -2.271897e-02
## H.npnct01.log H.npnct01.log 2.271577e-02
## S.T.take S.T.take -2.264447e-02
## H.P.on.this.day H.P.on.this.day -2.150663e-02
## S.P.first.draft S.P.first.draft -2.150663e-02
## A.P.first.draft A.P.first.draft -2.150663e-02
## S.npnct14.log S.npnct14.log -2.121844e-02
## H.T.test H.T.test -2.117852e-02
## H.npnct02.log H.npnct02.log -2.001851e-02
## S.npnct20.log S.npnct20.log -1.923169e-02
## A.npnct20.log A.npnct20.log -1.923169e-02
## A.T.obama A.T.obama -1.914924e-02
## PubDate.month.fctr PubDate.month.fctr 1.914874e-02
## S.T.obama S.T.obama -1.914281e-02
## A.T.said A.T.said 1.876762e-02
## S.T.said S.T.said 1.863436e-02
## S.P.year.colon S.P.year.colon -1.755336e-02
## A.P.year.colon A.P.year.colon -1.755336e-02
## PubDate.POSIX PubDate.POSIX 1.568326e-02
## PubDate.zoo PubDate.zoo 1.568326e-02
## A.npnct21.log A.npnct21.log 1.537569e-02
## A.npnct23.log A.npnct23.log 1.537569e-02
## A.npnct17.log A.npnct17.log -1.457558e-02
## A.npnct02.log A.npnct02.log -1.451467e-02
## H.T.big H.T.big -1.438162e-02
## H.T.word H.T.word -1.382927e-02
## A.npnct03.log A.npnct03.log -1.359260e-02
## H.T.make H.T.make 1.349595e-02
## H.npnct11.log H.npnct11.log 1.333613e-02
## H.npnct12.log H.npnct12.log -1.305305e-02
## A.P.http A.P.http -1.294748e-02
## A.npnct18.log A.npnct18.log -1.271661e-02
## S.npnct03.log S.npnct03.log -1.240734e-02
## S.npnct07.log S.npnct07.log -1.214357e-02
## A.npnct07.log A.npnct07.log -1.214357e-02
## H.npnct07.log H.npnct07.log -1.201741e-02
## PubDate.second.fctr PubDate.second.fctr -1.187946e-02
## UniqueID UniqueID 1.182492e-02
## PubDate.date.fctr PubDate.date.fctr -1.164756e-02
## A.T.one A.T.one 1.051414e-02
## S.T.one S.T.one 1.050293e-02
## H.T.bank H.T.bank -1.037439e-02
## H.T.obama H.T.obama -9.878461e-03
## H.T.say H.T.say -9.763205e-03
## H.P.friday.night.music H.P.friday.night.music -9.653967e-03
## H.npnct05.log H.npnct05.log -9.653967e-03
## H.npnct03.log H.npnct03.log 9.533020e-03
## .rnorm .rnorm -8.244230e-03
## H.P.s.notebook H.P.s.notebook 7.755542e-03
## PubDate.last100.log PubDate.last100.log -7.663322e-03
## H.npnct10.log H.npnct10.log -5.547032e-03
## H.npnct20.log H.npnct20.log -5.547032e-03
## S.npnct02.log S.npnct02.log -5.547032e-03
## S.npnct10.log S.npnct10.log -5.547032e-03
## A.npnct10.log A.npnct10.log -5.547032e-03
## A.npnct25.log A.npnct25.log -5.547032e-03
## A.npnct08.log A.npnct08.log -4.193476e-03
## S.npnct08.log S.npnct08.log -3.372706e-03
## S.T.presid S.T.presid -2.381159e-03
## A.T.presid A.T.presid -2.090565e-03
## S.npnct16.log S.npnct16.log -1.587454e-03
## A.npnct16.log A.npnct16.log -1.587454e-03
## H.T.take H.T.take -1.263270e-03
## H.npnct24.log H.npnct24.log -9.890046e-19
## S.npnct24.log S.npnct24.log -9.890046e-19
## A.npnct24.log A.npnct24.log -9.890046e-19
## H.npnct09.log H.npnct09.log NA
## H.npnct17.log H.npnct17.log NA
## H.npnct18.log H.npnct18.log NA
## H.npnct21.log H.npnct21.log NA
## H.npnct22.log H.npnct22.log NA
## H.npnct23.log H.npnct23.log NA
## H.npnct25.log H.npnct25.log NA
## H.npnct26.log H.npnct26.log NA
## H.npnct27.log H.npnct27.log NA
## H.npnct29.log H.npnct29.log NA
## H.npnct30.log H.npnct30.log NA
## H.P.http H.P.http NA
## S.npnct05.log S.npnct05.log NA
## S.npnct09.log S.npnct09.log NA
## S.npnct17.log S.npnct17.log NA
## S.npnct18.log S.npnct18.log NA
## S.npnct22.log S.npnct22.log NA
## S.npnct25.log S.npnct25.log NA
## S.npnct26.log S.npnct26.log NA
## S.npnct27.log S.npnct27.log NA
## S.npnct29.log S.npnct29.log NA
## S.npnct30.log S.npnct30.log NA
## S.P.http S.P.http NA
## A.npnct05.log A.npnct05.log NA
## A.npnct09.log A.npnct09.log NA
## A.npnct22.log A.npnct22.log NA
## A.npnct26.log A.npnct26.log NA
## A.npnct27.log A.npnct27.log NA
## A.npnct29.log A.npnct29.log NA
## A.npnct30.log A.npnct30.log NA
## PubDate.year.fctr PubDate.year.fctr NA
## exclude.as.feat cor.y.abs
## Popular 1 1.000000e+00
## A.nuppr.log 0 2.720962e-01
## S.nuppr.log 0 2.718459e-01
## WordCount.log 0 2.649526e-01
## A.ratio.sum.TfIdf.nwrds 0 2.623865e-01
## S.ratio.sum.TfIdf.nwrds 0 2.622549e-01
## WordCount 1 2.575265e-01
## S.nwrds.unq.log 0 2.461670e-01
## A.nwrds.unq.log 0 2.460117e-01
## H.ratio.sum.TfIdf.nwrds 0 2.254527e-01
## S.nchrs.log 0 2.246930e-01
## A.nchrs.log 0 2.245488e-01
## H.nwrds.unq.log 0 2.014127e-01
## A.nwrds.log 0 1.978712e-01
## S.nwrds.log 0 1.978341e-01
## H.nchrs.log 0 1.710624e-01
## myCategory.fctr 0 1.704362e-01
## H.nwrds.log 0 1.573431e-01
## PubDate.day.minutes.poly.1 0 1.567535e-01
## H.sum.TfIdf 0 1.520414e-01
## S.sum.TfIdf 0 1.484963e-01
## A.sum.TfIdf 0 1.478461e-01
## PubDate.hour.fctr 0 1.354368e-01
## H.npnct19.log 0 1.283641e-01
## H.nuppr.log 0 1.278085e-01
## A.ndgts.log 0 1.249484e-01
## S.ndgts.log 0 1.242046e-01
## A.ratio.nstopwrds.nwrds 0 1.213545e-01
## S.ratio.nstopwrds.nwrds 0 1.206896e-01
## H.ndgts.log 0 1.196633e-01
## A.nstopwrds.log 0 1.153879e-01
## S.nstopwrds.log 0 1.148150e-01
## PubDate.wkend 0 1.067288e-01
## A.npnct11.log 0 9.183870e-02
## S.npnct11.log 0 9.158156e-02
## H.P.recap.colon 0 9.008096e-02
## H.npnct28.log 0 8.917338e-02
## .clusterid 1 8.874584e-02
## .clusterid.fctr 0 8.874584e-02
## H.P.quandary 0 8.734922e-02
## H.nstopwrds.log 0 8.657067e-02
## S.T.week 0 8.552704e-02
## A.T.week 0 8.542792e-02
## S.T.fashion 0 8.417159e-02
## A.T.fashion 0 8.416793e-02
## H.npnct15.log 0 8.273237e-02
## H.T.fashion 0 7.947505e-02
## H.P.year.colon 0 7.842875e-02
## H.P.fashion.week 0 7.632046e-02
## PubDate.day.minutes.poly.4 0 7.394139e-02
## S.T.archiv 0 7.202808e-02
## A.T.archiv 0 7.202808e-02
## PubDate.day.minutes.poly.2 0 7.097772e-02
## S.P.fashion.week 0 7.080716e-02
## A.P.fashion.week 0 7.080716e-02
## S.T.tribun 0 7.013418e-02
## A.T.tribun 0 7.013418e-02
## S.T.intern 0 6.956906e-02
## A.T.intern 0 6.953025e-02
## A.npnct15.log 0 6.893301e-02
## S.T.photo 0 6.874283e-02
## A.T.photo 0 6.873838e-02
## H.T.week 0 6.827601e-02
## S.npnct15.log 0 6.770952e-02
## S.T.herald 0 6.752419e-02
## A.T.herald 0 6.752419e-02
## H.T.X2015 0 6.601141e-02
## H.T.daili 0 6.303731e-02
## S.npnct04.log 0 6.294642e-02
## A.npnct04.log 0 6.294642e-02
## H.T.report 0 6.238114e-02
## S.T.diari 0 6.229931e-02
## A.T.diari 0 6.229931e-02
## H.npnct14.log 0 6.158577e-02
## H.P.no.comment.colon 0 6.074669e-02
## H.T.day 0 6.044381e-02
## H.T.springsumm 0 5.943248e-02
## H.T.today 0 5.833786e-02
## PubDate.day.minutes.poly.5 0 5.592923e-02
## H.T.newyork 0 5.564999e-02
## S.npnct19.log 0 5.503894e-02
## A.npnct19.log 0 5.482747e-02
## S.T.articl 0 5.471737e-02
## A.T.articl 0 5.470831e-02
## H.P.facts.figures 0 5.410097e-02
## PubDate.last10 1 5.398093e-02
## H.npnct08.log 0 5.375262e-02
## S.npnct13.log 0 5.332519e-02
## H.npnct04.log 0 5.126277e-02
## S.T.share 0 5.105597e-02
## A.T.share 0 5.105597e-02
## A.npnct13.log 0 4.999563e-02
## S.T.newyorktim 0 4.985328e-02
## A.T.newyorktim 0 4.984782e-02
## PubDate.last10.log 0 4.931702e-02
## H.T.busi 0 4.899819e-02
## H.T.morn 0 4.838380e-02
## S.T.word 0 4.822452e-02
## A.T.word 0 4.821561e-02
## S.T.compani 0 4.787994e-02
## S.T.report 0 4.779877e-02
## A.T.compani 0 4.774812e-02
## A.T.report 0 4.774593e-02
## S.T.newyork 0 4.694998e-02
## A.T.newyork 0 4.686921e-02
## PubDate.last1.log 0 4.635751e-02
## H.T.X2014 0 4.523858e-02
## H.T.first 0 4.472902e-02
## S.T.first 0 4.447317e-02
## H.T.news 0 4.436368e-02
## A.T.first 0 4.433630e-02
## H.P.readers.respond 0 4.432886e-02
## H.T.clip 0 4.388279e-02
## H.P.daily.clip.report 0 4.388279e-02
## S.P.daily.clip.report 0 4.388279e-02
## A.P.daily.clip.report 0 4.388279e-02
## A.npnct28.log 0 4.373349e-02
## S.npnct28.log 0 4.370037e-02
## H.P.first.draft 0 4.316253e-02
## A.T.day 0 4.270831e-02
## S.T.day 0 4.262213e-02
## H.P.today.in.smallbusiness 0 4.243051e-02
## A.T.show 0 4.185292e-02
## S.T.show 0 4.182920e-02
## S.T.senat 0 4.143422e-02
## A.T.senat 0 4.139980e-02
## A.T.make 0 4.124187e-02
## S.T.make 0 4.118050e-02
## H.T.new 0 4.111696e-02
## H.ratio.nstopwrds.nwrds 0 4.024406e-02
## H.T.pictur 0 4.003882e-02
## PubDate.last100 1 3.989229e-02
## PubDate.wkday.fctr 0 3.980129e-02
## S.T.appear 0 3.941362e-02
## A.T.appear 0 3.941362e-02
## S.T.will 0 3.888838e-02
## A.T.will 0 3.884318e-02
## H.P.what.we.are 0 3.775209e-02
## A.npnct12.log 0 3.760012e-02
## S.T.year 0 3.756011e-02
## A.T.year 0 3.741571e-02
## H.P.today.in.politic 0 3.733661e-02
## S.npnct12.log 0 3.638891e-02
## PubDate.last1 1 3.592267e-02
## H.T.read 0 3.467043e-02
## PubDate.minute.fctr 0 3.407385e-02
## H.T.get 0 3.300192e-02
## H.T.art 0 3.291486e-02
## H.T.china 0 3.283653e-02
## H.P.verbatim.colon 0 3.194363e-02
## H.npnct06.log 0 3.190718e-02
## S.npnct01.log 0 3.093101e-02
## A.npnct01.log 0 3.093101e-02
## A.T.can 0 3.083389e-02
## H.T.polit 0 3.058564e-02
## H.npnct16.log 0 3.039622e-02
## S.T.can 0 3.005998e-02
## S.P.metropolitan.diary.colon 0 2.841404e-02
## A.P.metropolitan.diary.colon 0 2.841404e-02
## PubDate.day.minutes.poly.3 0 2.798355e-02
## H.T.billion 0 2.776561e-02
## S.npnct21.log 0 2.760321e-02
## S.npnct23.log 0 2.760321e-02
## H.T.ebola 0 2.682920e-02
## A.T.new 0 2.597887e-02
## S.T.new 0 2.592872e-02
## H.T.deal 0 2.556237e-02
## H.npnct13.log 0 2.524770e-02
## H.T.newyorktim 0 2.514415e-02
## A.T.time 0 2.430509e-02
## S.T.time 0 2.416246e-02
## A.npnct14.log 0 2.407715e-02
## S.npnct06.log 0 2.389145e-02
## A.npnct06.log 0 2.389145e-02
## A.T.take 0 2.271897e-02
## H.npnct01.log 0 2.271577e-02
## S.T.take 0 2.264447e-02
## H.P.on.this.day 0 2.150663e-02
## S.P.first.draft 0 2.150663e-02
## A.P.first.draft 0 2.150663e-02
## S.npnct14.log 0 2.121844e-02
## H.T.test 0 2.117852e-02
## H.npnct02.log 0 2.001851e-02
## S.npnct20.log 0 1.923169e-02
## A.npnct20.log 0 1.923169e-02
## A.T.obama 0 1.914924e-02
## PubDate.month.fctr 1 1.914874e-02
## S.T.obama 0 1.914281e-02
## A.T.said 0 1.876762e-02
## S.T.said 0 1.863436e-02
## S.P.year.colon 0 1.755336e-02
## A.P.year.colon 0 1.755336e-02
## PubDate.POSIX 1 1.568326e-02
## PubDate.zoo 1 1.568326e-02
## A.npnct21.log 0 1.537569e-02
## A.npnct23.log 0 1.537569e-02
## A.npnct17.log 0 1.457558e-02
## A.npnct02.log 0 1.451467e-02
## H.T.big 0 1.438162e-02
## H.T.word 0 1.382927e-02
## A.npnct03.log 0 1.359260e-02
## H.T.make 0 1.349595e-02
## H.npnct11.log 0 1.333613e-02
## H.npnct12.log 0 1.305305e-02
## A.P.http 0 1.294748e-02
## A.npnct18.log 0 1.271661e-02
## S.npnct03.log 0 1.240734e-02
## S.npnct07.log 0 1.214357e-02
## A.npnct07.log 0 1.214357e-02
## H.npnct07.log 0 1.201741e-02
## PubDate.second.fctr 0 1.187946e-02
## UniqueID 1 1.182492e-02
## PubDate.date.fctr 0 1.164756e-02
## A.T.one 0 1.051414e-02
## S.T.one 0 1.050293e-02
## H.T.bank 0 1.037439e-02
## H.T.obama 0 9.878461e-03
## H.T.say 0 9.763205e-03
## H.P.friday.night.music 0 9.653967e-03
## H.npnct05.log 0 9.653967e-03
## H.npnct03.log 0 9.533020e-03
## .rnorm 0 8.244230e-03
## H.P.s.notebook 0 7.755542e-03
## PubDate.last100.log 0 7.663322e-03
## H.npnct10.log 0 5.547032e-03
## H.npnct20.log 0 5.547032e-03
## S.npnct02.log 0 5.547032e-03
## S.npnct10.log 0 5.547032e-03
## A.npnct10.log 0 5.547032e-03
## A.npnct25.log 0 5.547032e-03
## A.npnct08.log 0 4.193476e-03
## S.npnct08.log 0 3.372706e-03
## S.T.presid 0 2.381159e-03
## A.T.presid 0 2.090565e-03
## S.npnct16.log 0 1.587454e-03
## A.npnct16.log 0 1.587454e-03
## H.T.take 0 1.263270e-03
## H.npnct24.log 0 9.890046e-19
## S.npnct24.log 0 9.890046e-19
## A.npnct24.log 0 9.890046e-19
## H.npnct09.log 0 NA
## H.npnct17.log 0 NA
## H.npnct18.log 0 NA
## H.npnct21.log 0 NA
## H.npnct22.log 0 NA
## H.npnct23.log 0 NA
## H.npnct25.log 0 NA
## H.npnct26.log 0 NA
## H.npnct27.log 0 NA
## H.npnct29.log 0 NA
## H.npnct30.log 0 NA
## H.P.http 0 NA
## S.npnct05.log 0 NA
## S.npnct09.log 0 NA
## S.npnct17.log 0 NA
## S.npnct18.log 0 NA
## S.npnct22.log 0 NA
## S.npnct25.log 0 NA
## S.npnct26.log 0 NA
## S.npnct27.log 0 NA
## S.npnct29.log 0 NA
## S.npnct30.log 0 NA
## S.P.http 0 NA
## A.npnct05.log 0 NA
## A.npnct09.log 0 NA
## A.npnct22.log 0 NA
## A.npnct26.log 0 NA
## A.npnct27.log 0 NA
## A.npnct29.log 0 NA
## A.npnct30.log 0 NA
## PubDate.year.fctr 0 NA# sav_feats_df <- glb_feats_df; glb_feats_df <- sav_feats_df
print(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, entity_df=glb_trnobs_df,
rsp_var=glb_rsp_var)))## [1] "cor(A.npnct01.log, S.npnct01.log)=1.0000"
## [1] "cor(Popular.fctr, A.npnct01.log)=0.0309"
## [1] "cor(Popular.fctr, S.npnct01.log)=0.0309"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.npnct01.log as highly correlated with
## A.npnct01.log## [1] "cor(A.npnct04.log, S.npnct04.log)=1.0000"
## [1] "cor(Popular.fctr, A.npnct04.log)=-0.0629"
## [1] "cor(Popular.fctr, S.npnct04.log)=-0.0629"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.npnct04.log as highly correlated with
## A.npnct04.log## [1] "cor(A.npnct06.log, S.npnct06.log)=1.0000"
## [1] "cor(Popular.fctr, A.npnct06.log)=-0.0239"
## [1] "cor(Popular.fctr, S.npnct06.log)=-0.0239"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.npnct06.log as highly correlated with
## A.npnct06.log## [1] "cor(A.npnct07.log, S.npnct07.log)=1.0000"
## [1] "cor(Popular.fctr, A.npnct07.log)=-0.0121"
## [1] "cor(Popular.fctr, S.npnct07.log)=-0.0121"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.npnct07.log as highly correlated with
## A.npnct07.log## [1] "cor(A.npnct20.log, S.npnct20.log)=1.0000"
## [1] "cor(Popular.fctr, A.npnct20.log)=-0.0192"
## [1] "cor(Popular.fctr, S.npnct20.log)=-0.0192"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.npnct20.log as highly correlated with
## A.npnct20.log## [1] "cor(A.npnct21.log, A.npnct23.log)=1.0000"
## [1] "cor(Popular.fctr, A.npnct21.log)=0.0154"
## [1] "cor(Popular.fctr, A.npnct23.log)=0.0154"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.npnct23.log as highly correlated with
## A.npnct21.log## [1] "cor(A.P.daily.clip.report, H.P.daily.clip.report)=1.0000"
## [1] "cor(Popular.fctr, A.P.daily.clip.report)=-0.0439"
## [1] "cor(Popular.fctr, H.P.daily.clip.report)=-0.0439"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified H.P.daily.clip.report as highly correlated with
## A.P.daily.clip.report## [1] "cor(A.P.daily.clip.report, S.P.daily.clip.report)=1.0000"
## [1] "cor(Popular.fctr, A.P.daily.clip.report)=-0.0439"
## [1] "cor(Popular.fctr, S.P.daily.clip.report)=-0.0439"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.P.daily.clip.report as highly correlated with
## A.P.daily.clip.report## [1] "cor(A.P.fashion.week, S.P.fashion.week)=1.0000"
## [1] "cor(Popular.fctr, A.P.fashion.week)=-0.0708"
## [1] "cor(Popular.fctr, S.P.fashion.week)=-0.0708"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.P.fashion.week as highly correlated with
## A.P.fashion.week## [1] "cor(A.P.first.draft, S.P.first.draft)=1.0000"
## [1] "cor(Popular.fctr, A.P.first.draft)=-0.0215"
## [1] "cor(Popular.fctr, S.P.first.draft)=-0.0215"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.P.first.draft as highly correlated with
## A.P.first.draft## [1] "cor(A.P.metropolitan.diary.colon, S.P.metropolitan.diary.colon)=1.0000"
## [1] "cor(Popular.fctr, A.P.metropolitan.diary.colon)=-0.0284"
## [1] "cor(Popular.fctr, S.P.metropolitan.diary.colon)=-0.0284"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.P.metropolitan.diary.colon as highly
## correlated with A.P.metropolitan.diary.colon## [1] "cor(A.P.year.colon, S.P.year.colon)=1.0000"
## [1] "cor(Popular.fctr, A.P.year.colon)=-0.0176"
## [1] "cor(Popular.fctr, S.P.year.colon)=-0.0176"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.P.year.colon as highly correlated with
## A.P.year.colon## [1] "cor(A.T.appear, S.T.appear)=1.0000"
## [1] "cor(Popular.fctr, A.T.appear)=-0.0394"
## [1] "cor(Popular.fctr, S.T.appear)=-0.0394"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.T.appear as highly correlated with A.T.appear## [1] "cor(A.T.archiv, S.T.archiv)=1.0000"
## [1] "cor(Popular.fctr, A.T.archiv)=-0.0720"
## [1] "cor(Popular.fctr, S.T.archiv)=-0.0720"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.T.archiv as highly correlated with A.T.archiv## [1] "cor(A.T.diari, S.T.diari)=1.0000"
## [1] "cor(Popular.fctr, A.T.diari)=-0.0623"
## [1] "cor(Popular.fctr, S.T.diari)=-0.0623"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.T.diari as highly correlated with A.T.diari## [1] "cor(A.T.herald, S.T.herald)=1.0000"
## [1] "cor(Popular.fctr, A.T.herald)=-0.0675"
## [1] "cor(Popular.fctr, S.T.herald)=-0.0675"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.T.herald as highly correlated with A.T.herald## [1] "cor(A.T.share, S.T.share)=1.0000"
## [1] "cor(Popular.fctr, A.T.share)=-0.0511"
## [1] "cor(Popular.fctr, S.T.share)=-0.0511"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.T.share as highly correlated with A.T.share## [1] "cor(A.T.tribun, S.T.tribun)=1.0000"
## [1] "cor(Popular.fctr, A.T.tribun)=-0.0701"
## [1] "cor(Popular.fctr, S.T.tribun)=-0.0701"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.T.tribun as highly correlated with A.T.tribun## [1] "cor(S.npnct21.log, S.npnct23.log)=1.0000"
## [1] "cor(Popular.fctr, S.npnct21.log)=0.0276"
## [1] "cor(Popular.fctr, S.npnct23.log)=0.0276"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.npnct23.log as highly correlated with
## S.npnct21.log## [1] "cor(A.P.daily.clip.report, H.T.clip)=1.0000"
## [1] "cor(Popular.fctr, A.P.daily.clip.report)=-0.0439"
## [1] "cor(Popular.fctr, H.T.clip)=-0.0439"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified H.T.clip as highly correlated with
## A.P.daily.clip.report## [1] "cor(A.T.fashion, S.T.fashion)=1.0000"
## [1] "cor(Popular.fctr, A.T.fashion)=-0.0842"
## [1] "cor(Popular.fctr, S.T.fashion)=-0.0842"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified A.T.fashion as highly correlated with
## S.T.fashion## [1] "cor(A.T.photo, S.T.photo)=1.0000"
## [1] "cor(Popular.fctr, A.T.photo)=-0.0687"
## [1] "cor(Popular.fctr, S.T.photo)=-0.0687"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.T.photo as highly correlated with S.T.photo## [1] "cor(A.T.newyorktim, S.T.newyorktim)=1.0000"
## [1] "cor(Popular.fctr, A.T.newyorktim)=-0.0498"
## [1] "cor(Popular.fctr, S.T.newyorktim)=-0.0499"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.T.newyorktim as highly correlated with
## S.T.newyorktim## [1] "cor(A.T.word, S.T.word)=1.0000"
## [1] "cor(Popular.fctr, A.T.word)=-0.0482"
## [1] "cor(Popular.fctr, S.T.word)=-0.0482"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.T.word as highly correlated with S.T.word## [1] "cor(A.T.articl, S.T.articl)=1.0000"
## [1] "cor(Popular.fctr, A.T.articl)=-0.0547"
## [1] "cor(Popular.fctr, S.T.articl)=-0.0547"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.T.articl as highly correlated with S.T.articl## [1] "cor(A.T.show, S.T.show)=1.0000"
## [1] "cor(Popular.fctr, A.T.show)=-0.0419"
## [1] "cor(Popular.fctr, S.T.show)=-0.0418"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.T.show as highly correlated with A.T.show## [1] "cor(A.T.make, S.T.make)=1.0000"
## [1] "cor(Popular.fctr, A.T.make)=0.0412"
## [1] "cor(Popular.fctr, S.T.make)=0.0412"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.T.make as highly correlated with A.T.make## [1] "cor(A.T.intern, S.T.intern)=1.0000"
## [1] "cor(Popular.fctr, A.T.intern)=-0.0695"
## [1] "cor(Popular.fctr, S.T.intern)=-0.0696"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.T.intern as highly correlated with S.T.intern## [1] "cor(A.T.report, S.T.report)=1.0000"
## [1] "cor(Popular.fctr, A.T.report)=-0.0477"
## [1] "cor(Popular.fctr, S.T.report)=-0.0478"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.T.report as highly correlated with S.T.report## [1] "cor(A.T.week, S.T.week)=1.0000"
## [1] "cor(Popular.fctr, A.T.week)=-0.0854"
## [1] "cor(Popular.fctr, S.T.week)=-0.0855"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.T.week as highly correlated with S.T.week## [1] "cor(A.T.newyork, S.T.newyork)=0.9999"
## [1] "cor(Popular.fctr, A.T.newyork)=-0.0469"
## [1] "cor(Popular.fctr, S.T.newyork)=-0.0469"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified A.T.newyork as highly correlated with
## S.T.newyork## [1] "cor(A.T.said, S.T.said)=0.9999"
## [1] "cor(Popular.fctr, A.T.said)=0.0188"
## [1] "cor(Popular.fctr, S.T.said)=0.0186"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.T.said as highly correlated with A.T.said## [1] "cor(A.T.year, S.T.year)=0.9999"
## [1] "cor(Popular.fctr, A.T.year)=-0.0374"
## [1] "cor(Popular.fctr, S.T.year)=-0.0376"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.T.year as highly correlated with S.T.year## [1] "cor(A.ratio.sum.TfIdf.nwrds, S.ratio.sum.TfIdf.nwrds)=0.9999"
## [1] "cor(Popular.fctr, A.ratio.sum.TfIdf.nwrds)=0.2624"
## [1] "cor(Popular.fctr, S.ratio.sum.TfIdf.nwrds)=0.2623"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.ratio.sum.TfIdf.nwrds as highly correlated
## with A.ratio.sum.TfIdf.nwrds## [1] "cor(A.T.compani, S.T.compani)=0.9999"
## [1] "cor(Popular.fctr, A.T.compani)=-0.0477"
## [1] "cor(Popular.fctr, S.T.compani)=-0.0479"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified A.T.compani as highly correlated with
## S.T.compani## [1] "cor(A.T.first, S.T.first)=0.9999"
## [1] "cor(Popular.fctr, A.T.first)=-0.0443"
## [1] "cor(Popular.fctr, S.T.first)=-0.0445"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.T.first as highly correlated with S.T.first## [1] "cor(A.T.new, S.T.new)=0.9998"
## [1] "cor(Popular.fctr, A.T.new)=-0.0260"
## [1] "cor(Popular.fctr, S.T.new)=-0.0259"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.T.new as highly correlated with A.T.new## [1] "cor(A.npnct11.log, S.npnct11.log)=0.9997"
## [1] "cor(Popular.fctr, A.npnct11.log)=-0.0918"
## [1] "cor(Popular.fctr, S.npnct11.log)=-0.0916"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.npnct11.log as highly correlated with
## A.npnct11.log## [1] "cor(A.T.one, S.T.one)=0.9997"
## [1] "cor(Popular.fctr, A.T.one)=0.0105"
## [1] "cor(Popular.fctr, S.T.one)=0.0105"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.T.one as highly correlated with A.T.one## [1] "cor(A.T.take, S.T.take)=0.9997"
## [1] "cor(Popular.fctr, A.T.take)=-0.0227"
## [1] "cor(Popular.fctr, S.T.take)=-0.0226"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.T.take as highly correlated with A.T.take## [1] "cor(A.sum.TfIdf, S.sum.TfIdf)=0.9997"
## [1] "cor(Popular.fctr, A.sum.TfIdf)=0.1478"
## [1] "cor(Popular.fctr, S.sum.TfIdf)=0.1485"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified A.sum.TfIdf as highly correlated with
## S.sum.TfIdf## [1] "cor(A.T.can, S.T.can)=0.9996"
## [1] "cor(Popular.fctr, A.T.can)=0.0308"
## [1] "cor(Popular.fctr, S.T.can)=0.0301"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.T.can as highly correlated with A.T.can## [1] "cor(A.T.will, S.T.will)=0.9996"
## [1] "cor(Popular.fctr, A.T.will)=-0.0388"
## [1] "cor(Popular.fctr, S.T.will)=-0.0389"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.T.will as highly correlated with S.T.will## [1] "cor(A.T.day, S.T.day)=0.9996"
## [1] "cor(Popular.fctr, A.T.day)=-0.0427"
## [1] "cor(Popular.fctr, S.T.day)=-0.0426"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.T.day as highly correlated with A.T.day## [1] "cor(A.T.time, S.T.time)=0.9996"
## [1] "cor(Popular.fctr, A.T.time)=-0.0243"
## [1] "cor(Popular.fctr, S.T.time)=-0.0242"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.T.time as highly correlated with A.T.time## [1] "cor(A.T.senat, S.T.senat)=0.9996"
## [1] "cor(Popular.fctr, A.T.senat)=-0.0414"
## [1] "cor(Popular.fctr, S.T.senat)=-0.0414"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.T.senat as highly correlated with S.T.senat## [1] "cor(A.T.obama, S.T.obama)=0.9995"
## [1] "cor(Popular.fctr, A.T.obama)=-0.0191"
## [1] "cor(Popular.fctr, S.T.obama)=-0.0191"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.T.obama as highly correlated with A.T.obama## [1] "cor(A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds)=0.9994"
## [1] "cor(Popular.fctr, A.ratio.nstopwrds.nwrds)=0.1214"
## [1] "cor(Popular.fctr, S.ratio.nstopwrds.nwrds)=0.1207"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.ratio.nstopwrds.nwrds as highly correlated
## with A.ratio.nstopwrds.nwrds## [1] "cor(A.nuppr.log, S.nuppr.log)=0.9991"
## [1] "cor(Popular.fctr, A.nuppr.log)=-0.2721"
## [1] "cor(Popular.fctr, S.nuppr.log)=-0.2718"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified S.nuppr.log as highly correlated with
## A.nuppr.log## [1] "cor(A.nwrds.unq.log, S.nwrds.unq.log)=0.9989"
## [1] "cor(Popular.fctr, A.nwrds.unq.log)=-0.2460"
## [1] "cor(Popular.fctr, S.nwrds.unq.log)=-0.2462"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.nwrds.unq.log as highly correlated with
## S.nwrds.unq.log## [1] "cor(A.npnct28.log, S.npnct28.log)=0.9989"
## [1] "cor(Popular.fctr, A.npnct28.log)=-0.0437"
## [1] "cor(Popular.fctr, S.npnct28.log)=-0.0437"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.npnct28.log as highly correlated with
## A.npnct28.log## [1] "cor(A.nstopwrds.log, S.nstopwrds.log)=0.9989"
## [1] "cor(Popular.fctr, A.nstopwrds.log)=-0.1154"
## [1] "cor(Popular.fctr, S.nstopwrds.log)=-0.1148"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.nstopwrds.log as highly correlated with
## A.nstopwrds.log## [1] "cor(A.nchrs.log, S.nchrs.log)=0.9986"
## [1] "cor(Popular.fctr, A.nchrs.log)=-0.2245"
## [1] "cor(Popular.fctr, S.nchrs.log)=-0.2247"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified A.nchrs.log as highly correlated with
## S.nchrs.log## [1] "cor(A.nwrds.log, S.nwrds.log)=0.9985"
## [1] "cor(Popular.fctr, A.nwrds.log)=-0.1979"
## [1] "cor(Popular.fctr, S.nwrds.log)=-0.1978"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified S.nwrds.log as highly correlated with
## A.nwrds.log## [1] "cor(A.npnct19.log, S.npnct19.log)=0.9957"
## [1] "cor(Popular.fctr, A.npnct19.log)=0.0548"
## [1] "cor(Popular.fctr, S.npnct19.log)=0.0550"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.npnct19.log as highly correlated with
## S.npnct19.log## [1] "cor(A.ndgts.log, S.ndgts.log)=0.9955"
## [1] "cor(Popular.fctr, A.ndgts.log)=-0.1249"
## [1] "cor(Popular.fctr, S.ndgts.log)=-0.1242"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified S.ndgts.log as highly correlated with
## A.ndgts.log## [1] "cor(A.npnct12.log, S.npnct12.log)=0.9935"
## [1] "cor(Popular.fctr, A.npnct12.log)=-0.0376"
## [1] "cor(Popular.fctr, S.npnct12.log)=-0.0364"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.npnct12.log as highly correlated with
## A.npnct12.log## [1] "cor(A.T.herald, A.T.tribun)=0.9935"
## [1] "cor(Popular.fctr, A.T.herald)=-0.0675"
## [1] "cor(Popular.fctr, A.T.tribun)=-0.0701"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.T.herald as highly correlated with A.T.tribun## [1] "cor(A.npnct15.log, S.npnct15.log)=0.9917"
## [1] "cor(Popular.fctr, A.npnct15.log)=-0.0689"
## [1] "cor(Popular.fctr, S.npnct15.log)=-0.0677"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.npnct15.log as highly correlated with
## A.npnct15.log## [1] "cor(A.npnct18.log, A.P.http)=0.9882"
## [1] "cor(Popular.fctr, A.npnct18.log)=-0.0127"
## [1] "cor(Popular.fctr, A.P.http)=-0.0129"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.npnct18.log as highly correlated with
## A.P.http## [1] "cor(A.npnct13.log, S.npnct13.log)=0.9795"
## [1] "cor(Popular.fctr, A.npnct13.log)=-0.0500"
## [1] "cor(Popular.fctr, S.npnct13.log)=-0.0533"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.npnct13.log as highly correlated with
## S.npnct13.log## [1] "cor(A.nwrds.log, S.nchrs.log)=0.9717"
## [1] "cor(Popular.fctr, A.nwrds.log)=-0.1979"
## [1] "cor(Popular.fctr, S.nchrs.log)=-0.2247"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified A.nwrds.log as highly correlated with
## S.nchrs.log## [1] "cor(A.T.archiv, A.T.tribun)=0.9663"
## [1] "cor(Popular.fctr, A.T.archiv)=-0.0720"
## [1] "cor(Popular.fctr, A.T.tribun)=-0.0701"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.T.tribun as highly correlated with A.T.archiv## [1] "cor(H.npnct14.log, H.T.springsumm)=0.9650"
## [1] "cor(Popular.fctr, H.npnct14.log)=-0.0616"
## [1] "cor(Popular.fctr, H.T.springsumm)=-0.0594"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified H.T.springsumm as highly correlated with
## H.npnct14.log## [1] "cor(S.nchrs.log, S.nwrds.unq.log)=0.9537"
## [1] "cor(Popular.fctr, S.nchrs.log)=-0.2247"
## [1] "cor(Popular.fctr, S.nwrds.unq.log)=-0.2462"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified S.nchrs.log as highly correlated with
## S.nwrds.unq.log## [1] "cor(A.npnct02.log, A.P.http)=0.9261"
## [1] "cor(Popular.fctr, A.npnct02.log)=-0.0145"
## [1] "cor(Popular.fctr, A.P.http)=-0.0129"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified A.P.http as highly correlated with
## A.npnct02.log## [1] "cor(H.nchrs.log, H.nwrds.log)=0.9212"
## [1] "cor(Popular.fctr, H.nchrs.log)=-0.1711"
## [1] "cor(Popular.fctr, H.nwrds.log)=-0.1573"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified H.nwrds.log as highly correlated with
## H.nchrs.log## [1] "cor(A.npnct03.log, S.npnct03.log)=0.9128"
## [1] "cor(Popular.fctr, A.npnct03.log)=-0.0136"
## [1] "cor(Popular.fctr, S.npnct03.log)=-0.0124"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.npnct03.log as highly correlated with
## A.npnct03.log## [1] "cor(PubDate.day.minutes.poly.1, PubDate.hour.fctr)=0.9026"
## [1] "cor(Popular.fctr, PubDate.day.minutes.poly.1)=0.1568"
## [1] "cor(Popular.fctr, PubDate.hour.fctr)=0.1354"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified PubDate.hour.fctr as highly correlated with
## PubDate.day.minutes.poly.1## [1] "cor(H.nchrs.log, H.nwrds.unq.log)=0.8882"
## [1] "cor(Popular.fctr, H.nchrs.log)=-0.1711"
## [1] "cor(Popular.fctr, H.nwrds.unq.log)=-0.2014"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified H.nchrs.log as highly correlated with
## H.nwrds.unq.log## [1] "cor(A.T.archiv, S.T.intern)=0.8869"
## [1] "cor(Popular.fctr, A.T.archiv)=-0.0720"
## [1] "cor(Popular.fctr, S.T.intern)=-0.0696"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.T.intern as highly correlated with A.T.archiv## [1] "cor(A.P.daily.clip.report, H.T.daili)=0.8858"
## [1] "cor(Popular.fctr, A.P.daily.clip.report)=-0.0439"
## [1] "cor(Popular.fctr, H.T.daili)=-0.0630"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.P.daily.clip.report as highly correlated with
## H.T.daili## [1] "cor(H.npnct14.log, H.T.X2015)=0.8848"
## [1] "cor(Popular.fctr, H.npnct14.log)=-0.0616"
## [1] "cor(Popular.fctr, H.T.X2015)=-0.0660"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified H.npnct14.log as highly correlated with
## H.T.X2015## [1] "cor(A.npnct02.log, A.npnct17.log)=0.8745"
## [1] "cor(Popular.fctr, A.npnct02.log)=-0.0145"
## [1] "cor(Popular.fctr, A.npnct17.log)=-0.0146"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.npnct02.log as highly correlated with
## A.npnct17.log## [1] "cor(H.P.today.in.politic, H.T.polit)=0.8713"
## [1] "cor(Popular.fctr, H.P.today.in.politic)=-0.0373"
## [1] "cor(Popular.fctr, H.T.polit)=-0.0306"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified H.T.polit as highly correlated with
## H.P.today.in.politic## [1] "cor(A.T.archiv, H.P.year.colon)=0.8509"
## [1] "cor(Popular.fctr, A.T.archiv)=-0.0720"
## [1] "cor(Popular.fctr, H.P.year.colon)=-0.0784"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified A.T.archiv as highly correlated with
## H.P.year.colon## [1] "cor(H.P.what.we.are, H.T.read)=0.8479"
## [1] "cor(Popular.fctr, H.P.what.we.are)=-0.0378"
## [1] "cor(Popular.fctr, H.T.read)=-0.0347"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified H.T.read as highly correlated with
## H.P.what.we.are## [1] "cor(H.P.fashion.week, H.T.fashion)=0.8336"
## [1] "cor(Popular.fctr, H.P.fashion.week)=-0.0763"
## [1] "cor(Popular.fctr, H.T.fashion)=-0.0795"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified H.P.fashion.week as highly correlated with
## H.T.fashion## [1] "cor(A.npnct28.log, H.T.morn)=0.8327"
## [1] "cor(Popular.fctr, A.npnct28.log)=-0.0437"
## [1] "cor(Popular.fctr, H.T.morn)=-0.0484"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.npnct28.log as highly correlated with
## H.T.morn## [1] "cor(H.nuppr.log, H.nwrds.unq.log)=0.8295"
## [1] "cor(Popular.fctr, H.nuppr.log)=-0.1278"
## [1] "cor(Popular.fctr, H.nwrds.unq.log)=-0.2014"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified H.nuppr.log as highly correlated with
## H.nwrds.unq.log## [1] "cor(H.T.daili, H.T.report)=0.8248"
## [1] "cor(Popular.fctr, H.T.daili)=-0.0630"
## [1] "cor(Popular.fctr, H.T.report)=-0.0624"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified H.T.report as highly correlated with H.T.daili## [1] "cor(H.npnct06.log, H.npnct16.log)=0.8106"
## [1] "cor(Popular.fctr, H.npnct06.log)=0.0319"
## [1] "cor(Popular.fctr, H.npnct16.log)=0.0304"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified H.npnct16.log as highly correlated with
## H.npnct06.log## [1] "cor(H.P.today.in.politic, H.T.today)=0.8042"
## [1] "cor(Popular.fctr, H.P.today.in.politic)=-0.0373"
## [1] "cor(Popular.fctr, H.T.today)=-0.0583"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified H.P.today.in.politic as highly correlated with
## H.T.today## [1] "cor(A.P.fashion.week, S.T.fashion)=0.8031"
## [1] "cor(Popular.fctr, A.P.fashion.week)=-0.0708"
## [1] "cor(Popular.fctr, S.T.fashion)=-0.0842"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.P.fashion.week as highly correlated with
## S.T.fashion## [1] "cor(A.T.appear, H.T.word)=0.7960"
## [1] "cor(Popular.fctr, A.T.appear)=-0.0394"
## [1] "cor(Popular.fctr, H.T.word)=-0.0138"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified H.T.word as highly correlated with A.T.appear## [1] "cor(A.ratio.sum.TfIdf.nwrds, S.nwrds.unq.log)=-0.7701"
## [1] "cor(Popular.fctr, A.ratio.sum.TfIdf.nwrds)=0.2624"
## [1] "cor(Popular.fctr, S.nwrds.unq.log)=-0.2462"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified S.nwrds.unq.log as highly correlated with
## A.ratio.sum.TfIdf.nwrds## [1] "cor(A.npnct14.log, A.npnct17.log)=0.7663"
## [1] "cor(Popular.fctr, A.npnct14.log)=-0.0241"
## [1] "cor(Popular.fctr, A.npnct17.log)=-0.0146"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.npnct17.log as highly correlated with
## A.npnct14.log## [1] "cor(A.T.diari, H.T.X2015)=0.7526"
## [1] "cor(Popular.fctr, A.T.diari)=-0.0623"
## [1] "cor(Popular.fctr, H.T.X2015)=-0.0660"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.T.diari as highly correlated with H.T.X2015## [1] "cor(A.npnct21.log, S.npnct21.log)=0.7461"
## [1] "cor(Popular.fctr, A.npnct21.log)=0.0154"
## [1] "cor(Popular.fctr, S.npnct21.log)=0.0276"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.npnct21.log as highly correlated with
## S.npnct21.log## [1] "cor(H.T.X2015, S.T.fashion)=0.7294"
## [1] "cor(Popular.fctr, H.T.X2015)=-0.0660"
## [1] "cor(Popular.fctr, S.T.fashion)=-0.0842"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified H.T.X2015 as highly correlated with S.T.fashion## [1] "cor(H.P.first.draft, H.T.first)=0.7289"
## [1] "cor(Popular.fctr, H.P.first.draft)=-0.0432"
## [1] "cor(Popular.fctr, H.T.first)=-0.0447"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified H.P.first.draft as highly correlated with
## H.T.first## [1] "cor(A.nstopwrds.log, A.ratio.sum.TfIdf.nwrds)=-0.7148"
## [1] "cor(Popular.fctr, A.nstopwrds.log)=-0.1154"
## [1] "cor(Popular.fctr, A.ratio.sum.TfIdf.nwrds)=0.2624"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnobs_df, : Identified A.nstopwrds.log as highly correlated with
## A.ratio.sum.TfIdf.nwrds## [1] "cor(H.npnct04.log, H.T.billion)=0.7051"
## [1] "cor(Popular.fctr, H.npnct04.log)=-0.0513"
## [1] "cor(Popular.fctr, H.T.billion)=-0.0278"## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df
## = glb_trnobs_df, : Identified H.T.billion as highly correlated with
## H.npnct04.log## id cor.y
## Popular Popular 1.000000e+00
## WordCount.log WordCount.log 2.649526e-01
## A.ratio.sum.TfIdf.nwrds A.ratio.sum.TfIdf.nwrds 2.623865e-01
## S.ratio.sum.TfIdf.nwrds S.ratio.sum.TfIdf.nwrds 2.622549e-01
## WordCount WordCount 2.575265e-01
## H.ratio.sum.TfIdf.nwrds H.ratio.sum.TfIdf.nwrds 2.254527e-01
## myCategory.fctr myCategory.fctr 1.704362e-01
## PubDate.day.minutes.poly.1 PubDate.day.minutes.poly.1 1.567535e-01
## H.sum.TfIdf H.sum.TfIdf 1.520414e-01
## S.sum.TfIdf S.sum.TfIdf 1.484963e-01
## A.sum.TfIdf A.sum.TfIdf 1.478461e-01
## PubDate.hour.fctr PubDate.hour.fctr 1.354368e-01
## H.npnct19.log H.npnct19.log 1.283641e-01
## A.ratio.nstopwrds.nwrds A.ratio.nstopwrds.nwrds 1.213545e-01
## S.ratio.nstopwrds.nwrds S.ratio.nstopwrds.nwrds 1.206896e-01
## PubDate.wkend PubDate.wkend 1.067288e-01
## H.P.recap.colon H.P.recap.colon 9.008096e-02
## H.P.quandary H.P.quandary 8.734922e-02
## PubDate.day.minutes.poly.4 PubDate.day.minutes.poly.4 7.394139e-02
## PubDate.day.minutes.poly.2 PubDate.day.minutes.poly.2 7.097772e-02
## H.P.no.comment.colon H.P.no.comment.colon 6.074669e-02
## S.npnct19.log S.npnct19.log 5.503894e-02
## A.npnct19.log A.npnct19.log 5.482747e-02
## H.P.facts.figures H.P.facts.figures 5.410097e-02
## PubDate.last10 PubDate.last10 5.398093e-02
## H.npnct08.log H.npnct08.log 5.375262e-02
## PubDate.last10.log PubDate.last10.log 4.931702e-02
## PubDate.last1.log PubDate.last1.log 4.635751e-02
## H.P.readers.respond H.P.readers.respond 4.432886e-02
## A.T.make A.T.make 4.124187e-02
## S.T.make S.T.make 4.118050e-02
## H.ratio.nstopwrds.nwrds H.ratio.nstopwrds.nwrds 4.024406e-02
## PubDate.last100 PubDate.last100 3.989229e-02
## PubDate.last1 PubDate.last1 3.592267e-02
## H.T.get H.T.get 3.300192e-02
## H.npnct06.log H.npnct06.log 3.190718e-02
## A.npnct01.log A.npnct01.log 3.093101e-02
## S.npnct01.log S.npnct01.log 3.093101e-02
## A.T.can A.T.can 3.083389e-02
## H.npnct16.log H.npnct16.log 3.039622e-02
## S.T.can S.T.can 3.005998e-02
## PubDate.day.minutes.poly.3 PubDate.day.minutes.poly.3 2.798355e-02
## S.npnct21.log S.npnct21.log 2.760321e-02
## S.npnct23.log S.npnct23.log 2.760321e-02
## H.T.ebola H.T.ebola 2.682920e-02
## H.npnct01.log H.npnct01.log 2.271577e-02
## PubDate.month.fctr PubDate.month.fctr 1.914874e-02
## A.T.said A.T.said 1.876762e-02
## S.T.said S.T.said 1.863436e-02
## PubDate.POSIX PubDate.POSIX 1.568326e-02
## PubDate.zoo PubDate.zoo 1.568326e-02
## A.npnct21.log A.npnct21.log 1.537569e-02
## A.npnct23.log A.npnct23.log 1.537569e-02
## H.T.make H.T.make 1.349595e-02
## H.npnct11.log H.npnct11.log 1.333613e-02
## UniqueID UniqueID 1.182492e-02
## A.T.one A.T.one 1.051414e-02
## S.T.one S.T.one 1.050293e-02
## H.npnct03.log H.npnct03.log 9.533020e-03
## H.P.s.notebook H.P.s.notebook 7.755542e-03
## A.npnct24.log A.npnct24.log -9.890046e-19
## H.npnct24.log H.npnct24.log -9.890046e-19
## S.npnct24.log S.npnct24.log -9.890046e-19
## H.T.take H.T.take -1.263270e-03
## A.npnct16.log A.npnct16.log -1.587454e-03
## S.npnct16.log S.npnct16.log -1.587454e-03
## A.T.presid A.T.presid -2.090565e-03
## S.T.presid S.T.presid -2.381159e-03
## S.npnct08.log S.npnct08.log -3.372706e-03
## A.npnct08.log A.npnct08.log -4.193476e-03
## A.npnct25.log A.npnct25.log -5.547032e-03
## A.npnct10.log A.npnct10.log -5.547032e-03
## H.npnct10.log H.npnct10.log -5.547032e-03
## H.npnct20.log H.npnct20.log -5.547032e-03
## S.npnct02.log S.npnct02.log -5.547032e-03
## S.npnct10.log S.npnct10.log -5.547032e-03
## PubDate.last100.log PubDate.last100.log -7.663322e-03
## .rnorm .rnorm -8.244230e-03
## H.npnct05.log H.npnct05.log -9.653967e-03
## H.P.friday.night.music H.P.friday.night.music -9.653967e-03
## H.T.say H.T.say -9.763205e-03
## H.T.obama H.T.obama -9.878461e-03
## H.T.bank H.T.bank -1.037439e-02
## PubDate.date.fctr PubDate.date.fctr -1.164756e-02
## PubDate.second.fctr PubDate.second.fctr -1.187946e-02
## H.npnct07.log H.npnct07.log -1.201741e-02
## A.npnct07.log A.npnct07.log -1.214357e-02
## S.npnct07.log S.npnct07.log -1.214357e-02
## S.npnct03.log S.npnct03.log -1.240734e-02
## A.npnct18.log A.npnct18.log -1.271661e-02
## A.P.http A.P.http -1.294748e-02
## H.npnct12.log H.npnct12.log -1.305305e-02
## A.npnct03.log A.npnct03.log -1.359260e-02
## H.T.word H.T.word -1.382927e-02
## H.T.big H.T.big -1.438162e-02
## A.npnct02.log A.npnct02.log -1.451467e-02
## A.npnct17.log A.npnct17.log -1.457558e-02
## A.P.year.colon A.P.year.colon -1.755336e-02
## S.P.year.colon S.P.year.colon -1.755336e-02
## S.T.obama S.T.obama -1.914281e-02
## A.T.obama A.T.obama -1.914924e-02
## A.npnct20.log A.npnct20.log -1.923169e-02
## S.npnct20.log S.npnct20.log -1.923169e-02
## H.npnct02.log H.npnct02.log -2.001851e-02
## H.T.test H.T.test -2.117852e-02
## S.npnct14.log S.npnct14.log -2.121844e-02
## A.P.first.draft A.P.first.draft -2.150663e-02
## H.P.on.this.day H.P.on.this.day -2.150663e-02
## S.P.first.draft S.P.first.draft -2.150663e-02
## S.T.take S.T.take -2.264447e-02
## A.T.take A.T.take -2.271897e-02
## A.npnct06.log A.npnct06.log -2.389145e-02
## S.npnct06.log S.npnct06.log -2.389145e-02
## A.npnct14.log A.npnct14.log -2.407715e-02
## S.T.time S.T.time -2.416246e-02
## A.T.time A.T.time -2.430509e-02
## H.T.newyorktim H.T.newyorktim -2.514415e-02
## H.npnct13.log H.npnct13.log -2.524770e-02
## H.T.deal H.T.deal -2.556237e-02
## S.T.new S.T.new -2.592872e-02
## A.T.new A.T.new -2.597887e-02
## H.T.billion H.T.billion -2.776561e-02
## A.P.metropolitan.diary.colon A.P.metropolitan.diary.colon -2.841404e-02
## S.P.metropolitan.diary.colon S.P.metropolitan.diary.colon -2.841404e-02
## H.T.polit H.T.polit -3.058564e-02
## H.P.verbatim.colon H.P.verbatim.colon -3.194363e-02
## H.T.china H.T.china -3.283653e-02
## H.T.art H.T.art -3.291486e-02
## PubDate.minute.fctr PubDate.minute.fctr -3.407385e-02
## H.T.read H.T.read -3.467043e-02
## S.npnct12.log S.npnct12.log -3.638891e-02
## H.P.today.in.politic H.P.today.in.politic -3.733661e-02
## A.T.year A.T.year -3.741571e-02
## S.T.year S.T.year -3.756011e-02
## A.npnct12.log A.npnct12.log -3.760012e-02
## H.P.what.we.are H.P.what.we.are -3.775209e-02
## A.T.will A.T.will -3.884318e-02
## S.T.will S.T.will -3.888838e-02
## A.T.appear A.T.appear -3.941362e-02
## S.T.appear S.T.appear -3.941362e-02
## PubDate.wkday.fctr PubDate.wkday.fctr -3.980129e-02
## H.T.pictur H.T.pictur -4.003882e-02
## H.T.new H.T.new -4.111696e-02
## A.T.senat A.T.senat -4.139980e-02
## S.T.senat S.T.senat -4.143422e-02
## S.T.show S.T.show -4.182920e-02
## A.T.show A.T.show -4.185292e-02
## H.P.today.in.smallbusiness H.P.today.in.smallbusiness -4.243051e-02
## S.T.day S.T.day -4.262213e-02
## A.T.day A.T.day -4.270831e-02
## H.P.first.draft H.P.first.draft -4.316253e-02
## S.npnct28.log S.npnct28.log -4.370037e-02
## A.npnct28.log A.npnct28.log -4.373349e-02
## A.P.daily.clip.report A.P.daily.clip.report -4.388279e-02
## H.P.daily.clip.report H.P.daily.clip.report -4.388279e-02
## H.T.clip H.T.clip -4.388279e-02
## S.P.daily.clip.report S.P.daily.clip.report -4.388279e-02
## A.T.first A.T.first -4.433630e-02
## H.T.news H.T.news -4.436368e-02
## S.T.first S.T.first -4.447317e-02
## H.T.first H.T.first -4.472902e-02
## H.T.X2014 H.T.X2014 -4.523858e-02
## A.T.newyork A.T.newyork -4.686921e-02
## S.T.newyork S.T.newyork -4.694998e-02
## A.T.report A.T.report -4.774593e-02
## A.T.compani A.T.compani -4.774812e-02
## S.T.report S.T.report -4.779877e-02
## S.T.compani S.T.compani -4.787994e-02
## A.T.word A.T.word -4.821561e-02
## S.T.word S.T.word -4.822452e-02
## H.T.morn H.T.morn -4.838380e-02
## H.T.busi H.T.busi -4.899819e-02
## A.T.newyorktim A.T.newyorktim -4.984782e-02
## S.T.newyorktim S.T.newyorktim -4.985328e-02
## A.npnct13.log A.npnct13.log -4.999563e-02
## A.T.share A.T.share -5.105597e-02
## S.T.share S.T.share -5.105597e-02
## H.npnct04.log H.npnct04.log -5.126277e-02
## S.npnct13.log S.npnct13.log -5.332519e-02
## A.T.articl A.T.articl -5.470831e-02
## S.T.articl S.T.articl -5.471737e-02
## H.T.newyork H.T.newyork -5.564999e-02
## PubDate.day.minutes.poly.5 PubDate.day.minutes.poly.5 -5.592923e-02
## H.T.today H.T.today -5.833786e-02
## H.T.springsumm H.T.springsumm -5.943248e-02
## H.T.day H.T.day -6.044381e-02
## H.npnct14.log H.npnct14.log -6.158577e-02
## A.T.diari A.T.diari -6.229931e-02
## S.T.diari S.T.diari -6.229931e-02
## H.T.report H.T.report -6.238114e-02
## A.npnct04.log A.npnct04.log -6.294642e-02
## S.npnct04.log S.npnct04.log -6.294642e-02
## H.T.daili H.T.daili -6.303731e-02
## H.T.X2015 H.T.X2015 -6.601141e-02
## A.T.herald A.T.herald -6.752419e-02
## S.T.herald S.T.herald -6.752419e-02
## S.npnct15.log S.npnct15.log -6.770952e-02
## H.T.week H.T.week -6.827601e-02
## A.T.photo A.T.photo -6.873838e-02
## S.T.photo S.T.photo -6.874283e-02
## A.npnct15.log A.npnct15.log -6.893301e-02
## A.T.intern A.T.intern -6.953025e-02
## S.T.intern S.T.intern -6.956906e-02
## A.T.tribun A.T.tribun -7.013418e-02
## S.T.tribun S.T.tribun -7.013418e-02
## A.P.fashion.week A.P.fashion.week -7.080716e-02
## S.P.fashion.week S.P.fashion.week -7.080716e-02
## A.T.archiv A.T.archiv -7.202808e-02
## S.T.archiv S.T.archiv -7.202808e-02
## H.P.fashion.week H.P.fashion.week -7.632046e-02
## H.P.year.colon H.P.year.colon -7.842875e-02
## H.T.fashion H.T.fashion -7.947505e-02
## H.npnct15.log H.npnct15.log -8.273237e-02
## A.T.fashion A.T.fashion -8.416793e-02
## S.T.fashion S.T.fashion -8.417159e-02
## A.T.week A.T.week -8.542792e-02
## S.T.week S.T.week -8.552704e-02
## H.nstopwrds.log H.nstopwrds.log -8.657067e-02
## .clusterid .clusterid -8.874584e-02
## .clusterid.fctr .clusterid.fctr -8.874584e-02
## H.npnct28.log H.npnct28.log -8.917338e-02
## S.npnct11.log S.npnct11.log -9.158156e-02
## A.npnct11.log A.npnct11.log -9.183870e-02
## S.nstopwrds.log S.nstopwrds.log -1.148150e-01
## A.nstopwrds.log A.nstopwrds.log -1.153879e-01
## H.ndgts.log H.ndgts.log -1.196633e-01
## S.ndgts.log S.ndgts.log -1.242046e-01
## A.ndgts.log A.ndgts.log -1.249484e-01
## H.nuppr.log H.nuppr.log -1.278085e-01
## H.nwrds.log H.nwrds.log -1.573431e-01
## H.nchrs.log H.nchrs.log -1.710624e-01
## S.nwrds.log S.nwrds.log -1.978341e-01
## A.nwrds.log A.nwrds.log -1.978712e-01
## H.nwrds.unq.log H.nwrds.unq.log -2.014127e-01
## A.nchrs.log A.nchrs.log -2.245488e-01
## S.nchrs.log S.nchrs.log -2.246930e-01
## A.nwrds.unq.log A.nwrds.unq.log -2.460117e-01
## S.nwrds.unq.log S.nwrds.unq.log -2.461670e-01
## S.nuppr.log S.nuppr.log -2.718459e-01
## A.nuppr.log A.nuppr.log -2.720962e-01
## A.npnct05.log A.npnct05.log NA
## A.npnct09.log A.npnct09.log NA
## A.npnct22.log A.npnct22.log NA
## A.npnct26.log A.npnct26.log NA
## A.npnct27.log A.npnct27.log NA
## A.npnct29.log A.npnct29.log NA
## A.npnct30.log A.npnct30.log NA
## H.npnct09.log H.npnct09.log NA
## H.npnct17.log H.npnct17.log NA
## H.npnct18.log H.npnct18.log NA
## H.npnct21.log H.npnct21.log NA
## H.npnct22.log H.npnct22.log NA
## H.npnct23.log H.npnct23.log NA
## H.npnct25.log H.npnct25.log NA
## H.npnct26.log H.npnct26.log NA
## H.npnct27.log H.npnct27.log NA
## H.npnct29.log H.npnct29.log NA
## H.npnct30.log H.npnct30.log NA
## H.P.http H.P.http NA
## PubDate.year.fctr PubDate.year.fctr NA
## S.npnct05.log S.npnct05.log NA
## S.npnct09.log S.npnct09.log NA
## S.npnct17.log S.npnct17.log NA
## S.npnct18.log S.npnct18.log NA
## S.npnct22.log S.npnct22.log NA
## S.npnct25.log S.npnct25.log NA
## S.npnct26.log S.npnct26.log NA
## S.npnct27.log S.npnct27.log NA
## S.npnct29.log S.npnct29.log NA
## S.npnct30.log S.npnct30.log NA
## S.P.http S.P.http NA
## exclude.as.feat cor.y.abs
## Popular 1 1.000000e+00
## WordCount.log 0 2.649526e-01
## A.ratio.sum.TfIdf.nwrds 0 2.623865e-01
## S.ratio.sum.TfIdf.nwrds 0 2.622549e-01
## WordCount 1 2.575265e-01
## H.ratio.sum.TfIdf.nwrds 0 2.254527e-01
## myCategory.fctr 0 1.704362e-01
## PubDate.day.minutes.poly.1 0 1.567535e-01
## H.sum.TfIdf 0 1.520414e-01
## S.sum.TfIdf 0 1.484963e-01
## A.sum.TfIdf 0 1.478461e-01
## PubDate.hour.fctr 0 1.354368e-01
## H.npnct19.log 0 1.283641e-01
## A.ratio.nstopwrds.nwrds 0 1.213545e-01
## S.ratio.nstopwrds.nwrds 0 1.206896e-01
## PubDate.wkend 0 1.067288e-01
## H.P.recap.colon 0 9.008096e-02
## H.P.quandary 0 8.734922e-02
## PubDate.day.minutes.poly.4 0 7.394139e-02
## PubDate.day.minutes.poly.2 0 7.097772e-02
## H.P.no.comment.colon 0 6.074669e-02
## S.npnct19.log 0 5.503894e-02
## A.npnct19.log 0 5.482747e-02
## H.P.facts.figures 0 5.410097e-02
## PubDate.last10 1 5.398093e-02
## H.npnct08.log 0 5.375262e-02
## PubDate.last10.log 0 4.931702e-02
## PubDate.last1.log 0 4.635751e-02
## H.P.readers.respond 0 4.432886e-02
## A.T.make 0 4.124187e-02
## S.T.make 0 4.118050e-02
## H.ratio.nstopwrds.nwrds 0 4.024406e-02
## PubDate.last100 1 3.989229e-02
## PubDate.last1 1 3.592267e-02
## H.T.get 0 3.300192e-02
## H.npnct06.log 0 3.190718e-02
## A.npnct01.log 0 3.093101e-02
## S.npnct01.log 0 3.093101e-02
## A.T.can 0 3.083389e-02
## H.npnct16.log 0 3.039622e-02
## S.T.can 0 3.005998e-02
## PubDate.day.minutes.poly.3 0 2.798355e-02
## S.npnct21.log 0 2.760321e-02
## S.npnct23.log 0 2.760321e-02
## H.T.ebola 0 2.682920e-02
## H.npnct01.log 0 2.271577e-02
## PubDate.month.fctr 1 1.914874e-02
## A.T.said 0 1.876762e-02
## S.T.said 0 1.863436e-02
## PubDate.POSIX 1 1.568326e-02
## PubDate.zoo 1 1.568326e-02
## A.npnct21.log 0 1.537569e-02
## A.npnct23.log 0 1.537569e-02
## H.T.make 0 1.349595e-02
## H.npnct11.log 0 1.333613e-02
## UniqueID 1 1.182492e-02
## A.T.one 0 1.051414e-02
## S.T.one 0 1.050293e-02
## H.npnct03.log 0 9.533020e-03
## H.P.s.notebook 0 7.755542e-03
## A.npnct24.log 0 9.890046e-19
## H.npnct24.log 0 9.890046e-19
## S.npnct24.log 0 9.890046e-19
## H.T.take 0 1.263270e-03
## A.npnct16.log 0 1.587454e-03
## S.npnct16.log 0 1.587454e-03
## A.T.presid 0 2.090565e-03
## S.T.presid 0 2.381159e-03
## S.npnct08.log 0 3.372706e-03
## A.npnct08.log 0 4.193476e-03
## A.npnct25.log 0 5.547032e-03
## A.npnct10.log 0 5.547032e-03
## H.npnct10.log 0 5.547032e-03
## H.npnct20.log 0 5.547032e-03
## S.npnct02.log 0 5.547032e-03
## S.npnct10.log 0 5.547032e-03
## PubDate.last100.log 0 7.663322e-03
## .rnorm 0 8.244230e-03
## H.npnct05.log 0 9.653967e-03
## H.P.friday.night.music 0 9.653967e-03
## H.T.say 0 9.763205e-03
## H.T.obama 0 9.878461e-03
## H.T.bank 0 1.037439e-02
## PubDate.date.fctr 0 1.164756e-02
## PubDate.second.fctr 0 1.187946e-02
## H.npnct07.log 0 1.201741e-02
## A.npnct07.log 0 1.214357e-02
## S.npnct07.log 0 1.214357e-02
## S.npnct03.log 0 1.240734e-02
## A.npnct18.log 0 1.271661e-02
## A.P.http 0 1.294748e-02
## H.npnct12.log 0 1.305305e-02
## A.npnct03.log 0 1.359260e-02
## H.T.word 0 1.382927e-02
## H.T.big 0 1.438162e-02
## A.npnct02.log 0 1.451467e-02
## A.npnct17.log 0 1.457558e-02
## A.P.year.colon 0 1.755336e-02
## S.P.year.colon 0 1.755336e-02
## S.T.obama 0 1.914281e-02
## A.T.obama 0 1.914924e-02
## A.npnct20.log 0 1.923169e-02
## S.npnct20.log 0 1.923169e-02
## H.npnct02.log 0 2.001851e-02
## H.T.test 0 2.117852e-02
## S.npnct14.log 0 2.121844e-02
## A.P.first.draft 0 2.150663e-02
## H.P.on.this.day 0 2.150663e-02
## S.P.first.draft 0 2.150663e-02
## S.T.take 0 2.264447e-02
## A.T.take 0 2.271897e-02
## A.npnct06.log 0 2.389145e-02
## S.npnct06.log 0 2.389145e-02
## A.npnct14.log 0 2.407715e-02
## S.T.time 0 2.416246e-02
## A.T.time 0 2.430509e-02
## H.T.newyorktim 0 2.514415e-02
## H.npnct13.log 0 2.524770e-02
## H.T.deal 0 2.556237e-02
## S.T.new 0 2.592872e-02
## A.T.new 0 2.597887e-02
## H.T.billion 0 2.776561e-02
## A.P.metropolitan.diary.colon 0 2.841404e-02
## S.P.metropolitan.diary.colon 0 2.841404e-02
## H.T.polit 0 3.058564e-02
## H.P.verbatim.colon 0 3.194363e-02
## H.T.china 0 3.283653e-02
## H.T.art 0 3.291486e-02
## PubDate.minute.fctr 0 3.407385e-02
## H.T.read 0 3.467043e-02
## S.npnct12.log 0 3.638891e-02
## H.P.today.in.politic 0 3.733661e-02
## A.T.year 0 3.741571e-02
## S.T.year 0 3.756011e-02
## A.npnct12.log 0 3.760012e-02
## H.P.what.we.are 0 3.775209e-02
## A.T.will 0 3.884318e-02
## S.T.will 0 3.888838e-02
## A.T.appear 0 3.941362e-02
## S.T.appear 0 3.941362e-02
## PubDate.wkday.fctr 0 3.980129e-02
## H.T.pictur 0 4.003882e-02
## H.T.new 0 4.111696e-02
## A.T.senat 0 4.139980e-02
## S.T.senat 0 4.143422e-02
## S.T.show 0 4.182920e-02
## A.T.show 0 4.185292e-02
## H.P.today.in.smallbusiness 0 4.243051e-02
## S.T.day 0 4.262213e-02
## A.T.day 0 4.270831e-02
## H.P.first.draft 0 4.316253e-02
## S.npnct28.log 0 4.370037e-02
## A.npnct28.log 0 4.373349e-02
## A.P.daily.clip.report 0 4.388279e-02
## H.P.daily.clip.report 0 4.388279e-02
## H.T.clip 0 4.388279e-02
## S.P.daily.clip.report 0 4.388279e-02
## A.T.first 0 4.433630e-02
## H.T.news 0 4.436368e-02
## S.T.first 0 4.447317e-02
## H.T.first 0 4.472902e-02
## H.T.X2014 0 4.523858e-02
## A.T.newyork 0 4.686921e-02
## S.T.newyork 0 4.694998e-02
## A.T.report 0 4.774593e-02
## A.T.compani 0 4.774812e-02
## S.T.report 0 4.779877e-02
## S.T.compani 0 4.787994e-02
## A.T.word 0 4.821561e-02
## S.T.word 0 4.822452e-02
## H.T.morn 0 4.838380e-02
## H.T.busi 0 4.899819e-02
## A.T.newyorktim 0 4.984782e-02
## S.T.newyorktim 0 4.985328e-02
## A.npnct13.log 0 4.999563e-02
## A.T.share 0 5.105597e-02
## S.T.share 0 5.105597e-02
## H.npnct04.log 0 5.126277e-02
## S.npnct13.log 0 5.332519e-02
## A.T.articl 0 5.470831e-02
## S.T.articl 0 5.471737e-02
## H.T.newyork 0 5.564999e-02
## PubDate.day.minutes.poly.5 0 5.592923e-02
## H.T.today 0 5.833786e-02
## H.T.springsumm 0 5.943248e-02
## H.T.day 0 6.044381e-02
## H.npnct14.log 0 6.158577e-02
## A.T.diari 0 6.229931e-02
## S.T.diari 0 6.229931e-02
## H.T.report 0 6.238114e-02
## A.npnct04.log 0 6.294642e-02
## S.npnct04.log 0 6.294642e-02
## H.T.daili 0 6.303731e-02
## H.T.X2015 0 6.601141e-02
## A.T.herald 0 6.752419e-02
## S.T.herald 0 6.752419e-02
## S.npnct15.log 0 6.770952e-02
## H.T.week 0 6.827601e-02
## A.T.photo 0 6.873838e-02
## S.T.photo 0 6.874283e-02
## A.npnct15.log 0 6.893301e-02
## A.T.intern 0 6.953025e-02
## S.T.intern 0 6.956906e-02
## A.T.tribun 0 7.013418e-02
## S.T.tribun 0 7.013418e-02
## A.P.fashion.week 0 7.080716e-02
## S.P.fashion.week 0 7.080716e-02
## A.T.archiv 0 7.202808e-02
## S.T.archiv 0 7.202808e-02
## H.P.fashion.week 0 7.632046e-02
## H.P.year.colon 0 7.842875e-02
## H.T.fashion 0 7.947505e-02
## H.npnct15.log 0 8.273237e-02
## A.T.fashion 0 8.416793e-02
## S.T.fashion 0 8.417159e-02
## A.T.week 0 8.542792e-02
## S.T.week 0 8.552704e-02
## H.nstopwrds.log 0 8.657067e-02
## .clusterid 1 8.874584e-02
## .clusterid.fctr 0 8.874584e-02
## H.npnct28.log 0 8.917338e-02
## S.npnct11.log 0 9.158156e-02
## A.npnct11.log 0 9.183870e-02
## S.nstopwrds.log 0 1.148150e-01
## A.nstopwrds.log 0 1.153879e-01
## H.ndgts.log 0 1.196633e-01
## S.ndgts.log 0 1.242046e-01
## A.ndgts.log 0 1.249484e-01
## H.nuppr.log 0 1.278085e-01
## H.nwrds.log 0 1.573431e-01
## H.nchrs.log 0 1.710624e-01
## S.nwrds.log 0 1.978341e-01
## A.nwrds.log 0 1.978712e-01
## H.nwrds.unq.log 0 2.014127e-01
## A.nchrs.log 0 2.245488e-01
## S.nchrs.log 0 2.246930e-01
## A.nwrds.unq.log 0 2.460117e-01
## S.nwrds.unq.log 0 2.461670e-01
## S.nuppr.log 0 2.718459e-01
## A.nuppr.log 0 2.720962e-01
## A.npnct05.log 0 NA
## A.npnct09.log 0 NA
## A.npnct22.log 0 NA
## A.npnct26.log 0 NA
## A.npnct27.log 0 NA
## A.npnct29.log 0 NA
## A.npnct30.log 0 NA
## H.npnct09.log 0 NA
## H.npnct17.log 0 NA
## H.npnct18.log 0 NA
## H.npnct21.log 0 NA
## H.npnct22.log 0 NA
## H.npnct23.log 0 NA
## H.npnct25.log 0 NA
## H.npnct26.log 0 NA
## H.npnct27.log 0 NA
## H.npnct29.log 0 NA
## H.npnct30.log 0 NA
## H.P.http 0 NA
## PubDate.year.fctr 0 NA
## S.npnct05.log 0 NA
## S.npnct09.log 0 NA
## S.npnct17.log 0 NA
## S.npnct18.log 0 NA
## S.npnct22.log 0 NA
## S.npnct25.log 0 NA
## S.npnct26.log 0 NA
## S.npnct27.log 0 NA
## S.npnct29.log 0 NA
## S.npnct30.log 0 NA
## S.P.http 0 NA
## cor.high.X freqRatio
## Popular <NA> 4.976212
## WordCount.log <NA> 1.266667
## A.ratio.sum.TfIdf.nwrds <NA> 2.583333
## S.ratio.sum.TfIdf.nwrds A.ratio.sum.TfIdf.nwrds 2.583333
## WordCount <NA> 2.315789
## H.ratio.sum.TfIdf.nwrds <NA> 1.148148
## myCategory.fctr <NA> 1.337185
## PubDate.day.minutes.poly.1 <NA> 1.225490
## H.sum.TfIdf <NA> 1.127273
## S.sum.TfIdf <NA> 2.583333
## A.sum.TfIdf S.sum.TfIdf 2.583333
## PubDate.hour.fctr PubDate.day.minutes.poly.1 1.835040
## H.npnct19.log <NA> 14.995098
## A.ratio.nstopwrds.nwrds <NA> 1.915094
## S.ratio.nstopwrds.nwrds A.ratio.nstopwrds.nwrds 1.908517
## PubDate.wkend <NA> 9.095827
## H.P.recap.colon <NA> 93.666667
## H.P.quandary <NA> 652.200000
## PubDate.day.minutes.poly.4 <NA> 1.225490
## PubDate.day.minutes.poly.2 <NA> 1.225490
## H.P.no.comment.colon <NA> 724.777778
## S.npnct19.log <NA> 12.862366
## A.npnct19.log S.npnct19.log 12.798715
## H.P.facts.figures <NA> 1087.666667
## PubDate.last10 <NA> 1.666667
## H.npnct08.log <NA> 111.620690
## PubDate.last10.log <NA> 1.666667
## PubDate.last1.log <NA> 1.142857
## H.P.readers.respond <NA> 342.789474
## A.T.make <NA> 273.782609
## S.T.make A.T.make 273.782609
## H.ratio.nstopwrds.nwrds <NA> 1.141631
## PubDate.last100 <NA> 25.000000
## PubDate.last1 <NA> 1.142857
## H.T.get <NA> 430.866667
## H.npnct06.log <NA> 68.935484
## A.npnct01.log <NA> 309.952381
## S.npnct01.log A.npnct01.log 309.952381
## A.T.can <NA> 261.666667
## H.npnct16.log H.npnct06.log 96.104478
## S.T.can A.T.can 261.666667
## PubDate.day.minutes.poly.3 <NA> 1.225490
## S.npnct21.log <NA> 6531.000000
## S.npnct23.log S.npnct21.log 6531.000000
## H.T.ebola <NA> 293.000000
## H.npnct01.log <NA> 282.913043
## PubDate.month.fctr <NA> 1.017514
## A.T.said <NA> 202.516129
## S.T.said A.T.said 202.516129
## PubDate.POSIX <NA> 1.000000
## PubDate.zoo <NA> 1.000000
## A.npnct21.log S.npnct21.log 3264.500000
## A.npnct23.log A.npnct21.log 3264.500000
## H.T.make <NA> 322.200000
## H.npnct11.log <NA> 4.937442
## UniqueID <NA> 1.000000
## A.T.one <NA> 214.931034
## S.T.one A.T.one 214.965517
## H.npnct03.log <NA> 2176.333333
## H.P.s.notebook <NA> 815.500000
## A.npnct24.log <NA> 0.000000
## H.npnct24.log <NA> 0.000000
## S.npnct24.log <NA> 0.000000
## H.T.take <NA> 322.250000
## A.npnct16.log <NA> 434.133333
## S.npnct16.log <NA> 434.133333
## A.T.presid <NA> 232.740741
## S.T.presid <NA> 232.740741
## S.npnct08.log <NA> 175.486486
## A.npnct08.log <NA> 170.842105
## A.npnct25.log <NA> 6531.000000
## A.npnct10.log <NA> 6531.000000
## H.npnct10.log <NA> 6531.000000
## H.npnct20.log <NA> 6531.000000
## S.npnct02.log <NA> 6531.000000
## S.npnct10.log <NA> 6531.000000
## PubDate.last100.log <NA> 25.000000
## .rnorm <NA> 2.000000
## H.npnct05.log <NA> 543.333333
## H.P.friday.night.music <NA> 543.333333
## H.T.say <NA> 247.461538
## H.T.obama <NA> 229.750000
## H.T.bank <NA> 214.300000
## PubDate.date.fctr <NA> 1.021394
## PubDate.second.fctr <NA> 1.018204
## H.npnct07.log <NA> 5.437234
## A.npnct07.log <NA> 1631.750000
## S.npnct07.log A.npnct07.log 1631.750000
## S.npnct03.log A.npnct03.log 1305.400000
## A.npnct18.log A.P.http 1631.500000
## A.P.http A.npnct02.log 1305.200000
## H.npnct12.log <NA> 13.126638
## A.npnct03.log <NA> 1087.666667
## H.T.word A.T.appear 104.096774
## H.T.big <NA> 403.562500
## A.npnct02.log A.npnct17.log 1087.500000
## A.npnct17.log A.npnct14.log 1087.500000
## A.P.year.colon <NA> 652.200000
## S.P.year.colon A.P.year.colon 652.200000
## S.T.obama A.T.obama 398.625000
## A.T.obama <NA> 398.625000
## A.npnct20.log <NA> 543.333333
## S.npnct20.log A.npnct20.log 543.333333
## H.npnct02.log <NA> 501.461538
## H.T.test <NA> 306.666667
## S.npnct14.log <NA> 203.062500
## A.P.first.draft <NA> 434.466667
## H.P.on.this.day <NA> 434.466667
## S.P.first.draft A.P.first.draft 434.466667
## S.T.take A.T.take 274.608696
## A.T.take <NA> 274.565217
## A.npnct06.log <NA> 115.642857
## S.npnct06.log A.npnct06.log 115.642857
## A.npnct14.log <NA> 196.696970
## S.T.time A.T.time 217.862069
## A.T.time <NA> 217.827586
## H.T.newyorktim <NA> 433.266667
## H.npnct13.log <NA> 22.802326
## H.T.deal <NA> 230.428571
## S.T.new A.T.new 114.423077
## A.T.new <NA> 114.403846
## H.T.billion H.npnct04.log 214.533333
## A.P.metropolitan.diary.colon <NA> 99.492308
## S.P.metropolitan.diary.colon A.P.metropolitan.diary.colon 99.492308
## H.T.polit H.P.today.in.politic 128.780000
## H.P.verbatim.colon <NA> 196.939394
## H.T.china <NA> 238.407407
## H.T.art <NA> 293.363636
## PubDate.minute.fctr <NA> 1.483365
## H.T.read H.P.what.we.are 179.388889
## S.npnct12.log A.npnct12.log 5.706263
## H.P.today.in.politic H.T.today 144.155556
## A.T.year S.T.year 160.815789
## S.T.year <NA> 160.815789
## A.npnct12.log <NA> 5.715368
## H.P.what.we.are <NA> 141.000000
## A.T.will S.T.will 121.734694
## S.T.will <NA> 119.340000
## A.T.appear <NA> 228.821429
## S.T.appear A.T.appear 228.821429
## PubDate.wkday.fctr <NA> 1.003268
## H.T.pictur <NA> 99.230769
## H.T.new <NA> 123.333333
## A.T.senat S.T.senat 372.294118
## S.T.senat <NA> 372.352941
## S.T.show A.T.show 274.608696
## A.T.show <NA> 263.166667
## H.P.today.in.smallbusiness <NA> 111.620690
## S.T.day A.T.day 89.528571
## A.T.day <NA> 89.514286
## H.P.first.draft H.T.first 107.866667
## S.npnct28.log A.npnct28.log 134.791667
## A.npnct28.log H.T.morn 126.862745
## A.P.daily.clip.report H.T.daili 104.354839
## H.P.daily.clip.report A.P.daily.clip.report 104.354839
## H.T.clip A.P.daily.clip.report 104.354839
## S.P.daily.clip.report A.P.daily.clip.report 104.354839
## A.T.first S.T.first 225.250000
## H.T.news <NA> 322.000000
## S.T.first <NA> 225.250000
## H.T.first <NA> 194.727273
## H.T.X2014 <NA> 110.879310
## A.T.newyork S.T.newyork 149.547619
## S.T.newyork <NA> 149.547619
## A.T.report S.T.report 80.371795
## A.T.compani S.T.compani 137.111111
## S.T.report <NA> 80.371795
## S.T.compani <NA> 137.111111
## A.T.word S.T.word 133.125000
## S.T.word <NA> 133.125000
## H.T.morn <NA> 165.205128
## H.T.busi <NA> 229.428571
## A.T.newyorktim S.T.newyorktim 84.540541
## S.T.newyorktim <NA> 84.540541
## A.npnct13.log S.npnct13.log 4.603330
## A.T.share <NA> 234.629630
## S.T.share A.T.share 234.629630
## H.npnct04.log <NA> 38.325301
## S.npnct13.log <NA> 4.672000
## A.T.articl S.T.articl 85.500000
## S.T.articl <NA> 85.500000
## H.T.newyork <NA> 112.517857
## PubDate.day.minutes.poly.5 <NA> 1.225490
## H.T.today <NA> 138.239130
## H.T.springsumm H.npnct14.log 106.966667
## H.T.day <NA> 86.547945
## H.npnct14.log H.T.X2015 52.983471
## A.T.diari H.T.X2015 71.528090
## S.T.diari A.T.diari 71.528090
## H.T.report H.T.daili 102.000000
## A.npnct04.log <NA> 28.536364
## S.npnct04.log A.npnct04.log 28.536364
## H.T.daili <NA> 102.903226
## H.T.X2015 S.T.fashion 96.833333
## A.T.herald A.T.tribun 144.750000
## S.T.herald A.T.herald 144.750000
## S.npnct15.log A.npnct15.log 13.647191
## H.T.week <NA> 71.352273
## A.T.photo S.T.photo 70.400000
## S.T.photo <NA> 70.400000
## A.npnct15.log <NA> 13.482222
## A.T.intern S.T.intern 140.400000
## S.T.intern A.T.archiv 140.400000
## A.T.tribun A.T.archiv 144.750000
## S.T.tribun A.T.tribun 144.750000
## A.P.fashion.week S.T.fashion 40.081761
## S.P.fashion.week A.P.fashion.week 40.081761
## A.T.archiv H.P.year.colon 144.545455
## S.T.archiv A.T.archiv 144.545455
## H.P.fashion.week H.T.fashion 34.500000
## H.P.year.colon <NA> 32.670103
## H.T.fashion <NA> 76.926829
## H.npnct15.log <NA> 3.914910
## A.T.fashion S.T.fashion 59.245283
## S.T.fashion <NA> 59.245283
## A.T.week S.T.week 56.560748
## S.T.week <NA> 56.560748
## H.nstopwrds.log <NA> 1.370729
## .clusterid <NA> 7.582627
## .clusterid.fctr <NA> 7.582627
## H.npnct28.log <NA> 24.123077
## S.npnct11.log A.npnct11.log 1.660473
## A.npnct11.log <NA> 1.660473
## S.nstopwrds.log A.nstopwrds.log 1.097879
## A.nstopwrds.log A.ratio.sum.TfIdf.nwrds 1.096091
## H.ndgts.log <NA> 13.616137
## S.ndgts.log A.ndgts.log 10.511247
## A.ndgts.log <NA> 10.501022
## H.nuppr.log H.nwrds.unq.log 1.033930
## H.nwrds.log H.nchrs.log 1.104308
## H.nchrs.log H.nwrds.unq.log 1.023810
## S.nwrds.log A.nwrds.log 1.049342
## A.nwrds.log S.nchrs.log 1.052805
## H.nwrds.unq.log <NA> 1.000000
## A.nchrs.log S.nchrs.log 1.328571
## S.nchrs.log S.nwrds.unq.log 1.328571
## A.nwrds.unq.log S.nwrds.unq.log 1.054206
## S.nwrds.unq.log A.ratio.sum.TfIdf.nwrds 1.054206
## S.nuppr.log A.nuppr.log 1.152620
## A.nuppr.log <NA> 1.151308
## A.npnct05.log <NA> 0.000000
## A.npnct09.log <NA> 0.000000
## A.npnct22.log <NA> 0.000000
## A.npnct26.log <NA> 0.000000
## A.npnct27.log <NA> 0.000000
## A.npnct29.log <NA> 0.000000
## A.npnct30.log <NA> 0.000000
## H.npnct09.log <NA> 0.000000
## H.npnct17.log <NA> 0.000000
## H.npnct18.log <NA> 0.000000
## H.npnct21.log <NA> 0.000000
## H.npnct22.log <NA> 0.000000
## H.npnct23.log <NA> 0.000000
## H.npnct25.log <NA> 0.000000
## H.npnct26.log <NA> 0.000000
## H.npnct27.log <NA> 0.000000
## H.npnct29.log <NA> 0.000000
## H.npnct30.log <NA> 0.000000
## H.P.http <NA> 0.000000
## PubDate.year.fctr <NA> 0.000000
## S.npnct05.log <NA> 0.000000
## S.npnct09.log <NA> 0.000000
## S.npnct17.log <NA> 0.000000
## S.npnct18.log <NA> 0.000000
## S.npnct22.log <NA> 0.000000
## S.npnct25.log <NA> 0.000000
## S.npnct26.log <NA> 0.000000
## S.npnct27.log <NA> 0.000000
## S.npnct29.log <NA> 0.000000
## S.npnct30.log <NA> 0.000000
## S.P.http <NA> 0.000000
## percentUnique zeroVar nzv myNearZV
## Popular 0.03061849 FALSE FALSE FALSE
## WordCount.log 24.14268218 FALSE FALSE FALSE
## A.ratio.sum.TfIdf.nwrds 94.51928965 FALSE FALSE FALSE
## S.ratio.sum.TfIdf.nwrds 94.45805266 FALSE FALSE FALSE
## WordCount 24.15799143 FALSE FALSE FALSE
## H.ratio.sum.TfIdf.nwrds 90.46233925 FALSE FALSE FALSE
## myCategory.fctr 0.30618494 FALSE FALSE FALSE
## PubDate.day.minutes.poly.1 18.08022045 FALSE FALSE FALSE
## H.sum.TfIdf 84.44580527 FALSE FALSE FALSE
## S.sum.TfIdf 94.32026944 FALSE FALSE FALSE
## A.sum.TfIdf 94.27434170 FALSE FALSE FALSE
## PubDate.hour.fctr 0.04592774 FALSE FALSE FALSE
## H.npnct19.log 0.06123699 FALSE FALSE FALSE
## A.ratio.nstopwrds.nwrds 4.10287814 FALSE FALSE FALSE
## S.ratio.nstopwrds.nwrds 3.75076546 FALSE FALSE FALSE
## PubDate.wkend 0.03061849 FALSE FALSE FALSE
## H.P.recap.colon 0.03061849 FALSE TRUE FALSE
## H.P.quandary 0.03061849 FALSE TRUE FALSE
## PubDate.day.minutes.poly.4 18.08022045 FALSE FALSE FALSE
## PubDate.day.minutes.poly.2 18.08022045 FALSE FALSE FALSE
## H.P.no.comment.colon 0.03061849 FALSE TRUE FALSE
## S.npnct19.log 0.07654623 FALSE FALSE FALSE
## A.npnct19.log 0.07654623 FALSE FALSE FALSE
## H.P.facts.figures 0.03061849 FALSE TRUE FALSE
## PubDate.last10 79.05695040 FALSE FALSE FALSE
## H.npnct08.log 0.03061849 FALSE TRUE FALSE
## PubDate.last10.log 79.05695040 FALSE FALSE FALSE
## PubDate.last1.log 36.49724434 FALSE FALSE FALSE
## H.P.readers.respond 0.03061849 FALSE TRUE FALSE
## A.T.make 0.44396816 FALSE TRUE FALSE
## S.T.make 0.44396816 FALSE TRUE FALSE
## H.ratio.nstopwrds.nwrds 0.96448255 FALSE FALSE FALSE
## PubDate.last100 92.52908757 FALSE FALSE FALSE
## PubDate.last1 36.49724434 FALSE FALSE FALSE
## H.T.get 0.18371096 FALSE TRUE FALSE
## H.npnct06.log 0.06123699 FALSE TRUE FALSE
## A.npnct01.log 0.06123699 FALSE TRUE FALSE
## S.npnct01.log 0.06123699 FALSE TRUE FALSE
## A.T.can 0.48989590 FALSE TRUE FALSE
## H.npnct16.log 0.06123699 FALSE TRUE FALSE
## S.T.can 0.41334966 FALSE TRUE FALSE
## PubDate.day.minutes.poly.3 18.08022045 FALSE FALSE FALSE
## S.npnct21.log 0.03061849 FALSE TRUE TRUE
## S.npnct23.log 0.03061849 FALSE TRUE TRUE
## H.T.ebola 0.16840171 FALSE TRUE FALSE
## H.npnct01.log 0.04592774 FALSE TRUE FALSE
## PubDate.month.fctr 0.04592774 FALSE FALSE FALSE
## A.T.said 0.41334966 FALSE TRUE FALSE
## S.T.said 0.38273117 FALSE TRUE FALSE
## PubDate.POSIX 99.86221678 FALSE FALSE FALSE
## PubDate.zoo 99.86221678 FALSE FALSE FALSE
## A.npnct21.log 0.04592774 FALSE TRUE TRUE
## A.npnct23.log 0.04592774 FALSE TRUE TRUE
## H.T.make 0.13778322 FALSE TRUE FALSE
## H.npnct11.log 0.07654623 FALSE FALSE FALSE
## UniqueID 100.00000000 FALSE FALSE FALSE
## A.T.one 0.48989590 FALSE TRUE FALSE
## S.T.one 0.44396816 FALSE TRUE FALSE
## H.npnct03.log 0.03061849 FALSE TRUE TRUE
## H.P.s.notebook 0.03061849 FALSE TRUE FALSE
## A.npnct24.log 0.01530925 TRUE TRUE TRUE
## H.npnct24.log 0.01530925 TRUE TRUE TRUE
## S.npnct24.log 0.01530925 TRUE TRUE TRUE
## H.T.take 0.15309247 FALSE TRUE FALSE
## A.npnct16.log 0.04592774 FALSE TRUE FALSE
## S.npnct16.log 0.04592774 FALSE TRUE FALSE
## A.T.presid 0.45927740 FALSE TRUE FALSE
## S.T.presid 0.42865891 FALSE TRUE FALSE
## S.npnct08.log 0.06123699 FALSE TRUE FALSE
## A.npnct08.log 0.06123699 FALSE TRUE FALSE
## A.npnct25.log 0.03061849 FALSE TRUE TRUE
## A.npnct10.log 0.03061849 FALSE TRUE TRUE
## H.npnct10.log 0.03061849 FALSE TRUE TRUE
## H.npnct20.log 0.03061849 FALSE TRUE TRUE
## S.npnct02.log 0.03061849 FALSE TRUE TRUE
## S.npnct10.log 0.03061849 FALSE TRUE TRUE
## PubDate.last100.log 92.19228414 FALSE FALSE FALSE
## .rnorm 99.98469075 FALSE FALSE FALSE
## H.npnct05.log 0.03061849 FALSE TRUE FALSE
## H.P.friday.night.music 0.03061849 FALSE TRUE FALSE
## H.T.say 0.16840171 FALSE TRUE FALSE
## H.T.obama 0.16840171 FALSE TRUE FALSE
## H.T.bank 0.13778322 FALSE TRUE FALSE
## PubDate.date.fctr 0.07654623 FALSE FALSE FALSE
## PubDate.second.fctr 0.06123699 FALSE FALSE FALSE
## H.npnct07.log 0.12247397 FALSE FALSE FALSE
## A.npnct07.log 0.04592774 FALSE TRUE FALSE
## S.npnct07.log 0.04592774 FALSE TRUE FALSE
## S.npnct03.log 0.03061849 FALSE TRUE FALSE
## A.npnct18.log 0.06123699 FALSE TRUE FALSE
## A.P.http 0.04592774 FALSE TRUE FALSE
## H.npnct12.log 0.09185548 FALSE FALSE FALSE
## A.npnct03.log 0.03061849 FALSE TRUE FALSE
## H.T.word 0.13778322 FALSE TRUE FALSE
## H.T.big 0.19902021 FALSE TRUE FALSE
## A.npnct02.log 0.04592774 FALSE TRUE FALSE
## A.npnct17.log 0.04592774 FALSE TRUE FALSE
## A.P.year.colon 0.03061849 FALSE TRUE FALSE
## S.P.year.colon 0.03061849 FALSE TRUE FALSE
## S.T.obama 0.38273117 FALSE TRUE FALSE
## A.T.obama 0.42865891 FALSE TRUE FALSE
## A.npnct20.log 0.03061849 FALSE TRUE FALSE
## S.npnct20.log 0.03061849 FALSE TRUE FALSE
## H.npnct02.log 0.03061849 FALSE TRUE FALSE
## H.T.test 0.13778322 FALSE TRUE FALSE
## S.npnct14.log 0.04592774 FALSE TRUE FALSE
## A.P.first.draft 0.03061849 FALSE TRUE FALSE
## H.P.on.this.day 0.03061849 FALSE TRUE FALSE
## S.P.first.draft 0.03061849 FALSE TRUE FALSE
## S.T.take 0.38273117 FALSE TRUE FALSE
## A.T.take 0.42865891 FALSE TRUE FALSE
## A.npnct06.log 0.03061849 FALSE TRUE FALSE
## S.npnct06.log 0.03061849 FALSE TRUE FALSE
## A.npnct14.log 0.10716473 FALSE TRUE FALSE
## S.T.time 0.42865891 FALSE TRUE FALSE
## A.T.time 0.42865891 FALSE TRUE FALSE
## H.T.newyorktim 0.12247397 FALSE TRUE FALSE
## H.npnct13.log 0.12247397 FALSE TRUE FALSE
## H.T.deal 0.13778322 FALSE TRUE FALSE
## S.T.new 0.47458665 FALSE TRUE FALSE
## A.T.new 0.48989590 FALSE TRUE FALSE
## H.T.billion 0.13778322 FALSE TRUE FALSE
## A.P.metropolitan.diary.colon 0.03061849 FALSE TRUE FALSE
## S.P.metropolitan.diary.colon 0.03061849 FALSE TRUE FALSE
## H.T.polit 0.13778322 FALSE TRUE FALSE
## H.P.verbatim.colon 0.03061849 FALSE TRUE FALSE
## H.T.china 0.16840171 FALSE TRUE FALSE
## H.T.art 0.19902021 FALSE TRUE FALSE
## PubDate.minute.fctr 0.06123699 FALSE FALSE FALSE
## H.T.read 0.16840171 FALSE TRUE FALSE
## S.npnct12.log 0.09185548 FALSE FALSE FALSE
## H.P.today.in.politic 0.03061849 FALSE TRUE FALSE
## A.T.year 0.48989590 FALSE TRUE FALSE
## S.T.year 0.45927740 FALSE TRUE FALSE
## A.npnct12.log 0.12247397 FALSE FALSE FALSE
## H.P.what.we.are 0.03061849 FALSE TRUE FALSE
## A.T.will 0.59706062 FALSE TRUE FALSE
## S.T.will 0.55113288 FALSE TRUE FALSE
## A.T.appear 0.30618494 FALSE TRUE FALSE
## S.T.appear 0.30618494 FALSE TRUE FALSE
## PubDate.wkday.fctr 0.10716473 FALSE FALSE FALSE
## H.T.pictur 0.10716473 FALSE TRUE FALSE
## H.T.new 0.19902021 FALSE TRUE FALSE
## A.T.senat 0.50520514 FALSE TRUE FALSE
## S.T.senat 0.47458665 FALSE TRUE FALSE
## S.T.show 0.38273117 FALSE TRUE FALSE
## A.T.show 0.39804042 FALSE TRUE FALSE
## H.P.today.in.smallbusiness 0.03061849 FALSE TRUE FALSE
## S.T.day 0.41334966 FALSE TRUE FALSE
## A.T.day 0.44396816 FALSE TRUE FALSE
## H.P.first.draft 0.03061849 FALSE TRUE FALSE
## S.npnct28.log 0.04592774 FALSE TRUE FALSE
## A.npnct28.log 0.04592774 FALSE TRUE FALSE
## A.P.daily.clip.report 0.03061849 FALSE TRUE FALSE
## H.P.daily.clip.report 0.03061849 FALSE TRUE FALSE
## H.T.clip 0.03061849 FALSE TRUE FALSE
## S.P.daily.clip.report 0.03061849 FALSE TRUE FALSE
## A.T.first 0.42865891 FALSE TRUE FALSE
## H.T.news 0.15309247 FALSE TRUE FALSE
## S.T.first 0.41334966 FALSE TRUE FALSE
## H.T.first 0.15309247 FALSE TRUE FALSE
## H.T.X2014 0.13778322 FALSE TRUE FALSE
## A.T.newyork 0.44396816 FALSE TRUE FALSE
## S.T.newyork 0.41334966 FALSE TRUE FALSE
## A.T.report 0.38273117 FALSE TRUE FALSE
## A.T.compani 0.48989590 FALSE TRUE FALSE
## S.T.report 0.35211268 FALSE TRUE FALSE
## S.T.compani 0.44396816 FALSE TRUE FALSE
## A.T.word 0.30618494 FALSE TRUE FALSE
## S.T.word 0.30618494 FALSE TRUE FALSE
## H.T.morn 0.07654623 FALSE TRUE FALSE
## H.T.busi 0.18371096 FALSE TRUE FALSE
## A.T.newyorktim 0.32149418 FALSE TRUE FALSE
## S.T.newyorktim 0.33680343 FALSE TRUE FALSE
## A.npnct13.log 0.16840171 FALSE FALSE FALSE
## A.T.share 0.38273117 FALSE TRUE FALSE
## S.T.share 0.38273117 FALSE TRUE FALSE
## H.npnct04.log 0.04592774 FALSE TRUE FALSE
## S.npnct13.log 0.16840171 FALSE FALSE FALSE
## A.T.articl 0.29087569 FALSE TRUE FALSE
## S.T.articl 0.29087569 FALSE TRUE FALSE
## H.T.newyork 0.15309247 FALSE TRUE FALSE
## PubDate.day.minutes.poly.5 18.08022045 FALSE FALSE FALSE
## H.T.today 0.13778322 FALSE TRUE FALSE
## H.T.springsumm 0.09185548 FALSE TRUE FALSE
## H.T.day 0.18371096 FALSE TRUE FALSE
## H.npnct14.log 0.03061849 FALSE TRUE FALSE
## A.T.diari 0.18371096 FALSE TRUE FALSE
## S.T.diari 0.18371096 FALSE TRUE FALSE
## H.T.report 0.16840171 FALSE TRUE FALSE
## A.npnct04.log 0.07654623 FALSE TRUE FALSE
## S.npnct04.log 0.07654623 FALSE TRUE FALSE
## H.T.daili 0.16840171 FALSE TRUE FALSE
## H.T.X2015 0.10716473 FALSE TRUE FALSE
## A.T.herald 0.24494795 FALSE TRUE FALSE
## S.T.herald 0.24494795 FALSE TRUE FALSE
## S.npnct15.log 0.04592774 FALSE FALSE FALSE
## H.T.week 0.16840171 FALSE TRUE FALSE
## A.T.photo 0.27556644 FALSE TRUE FALSE
## S.T.photo 0.29087569 FALSE TRUE FALSE
## A.npnct15.log 0.04592774 FALSE FALSE FALSE
## A.T.intern 0.32149418 FALSE TRUE FALSE
## S.T.intern 0.30618494 FALSE TRUE FALSE
## A.T.tribun 0.24494795 FALSE TRUE FALSE
## S.T.tribun 0.24494795 FALSE TRUE FALSE
## A.P.fashion.week 0.03061849 FALSE TRUE FALSE
## S.P.fashion.week 0.03061849 FALSE TRUE FALSE
## A.T.archiv 0.24494795 FALSE TRUE FALSE
## S.T.archiv 0.24494795 FALSE TRUE FALSE
## H.P.fashion.week 0.03061849 FALSE TRUE FALSE
## H.P.year.colon 0.03061849 FALSE TRUE FALSE
## H.T.fashion 0.19902021 FALSE TRUE FALSE
## H.npnct15.log 0.04592774 FALSE FALSE FALSE
## A.T.fashion 0.39804042 FALSE TRUE FALSE
## S.T.fashion 0.38273117 FALSE TRUE FALSE
## A.T.week 0.47458665 FALSE TRUE FALSE
## S.T.week 0.41334966 FALSE TRUE FALSE
## H.nstopwrds.log 0.12247397 FALSE FALSE FALSE
## .clusterid 0.30618494 FALSE FALSE FALSE
## .clusterid.fctr 0.30618494 FALSE FALSE FALSE
## H.npnct28.log 0.03061849 FALSE TRUE FALSE
## S.npnct11.log 0.13778322 FALSE FALSE FALSE
## A.npnct11.log 0.13778322 FALSE FALSE FALSE
## S.nstopwrds.log 0.38273117 FALSE FALSE FALSE
## A.nstopwrds.log 0.42865891 FALSE FALSE FALSE
## H.ndgts.log 0.18371096 FALSE FALSE FALSE
## S.ndgts.log 0.26025720 FALSE FALSE FALSE
## A.ndgts.log 0.29087569 FALSE FALSE FALSE
## H.nuppr.log 0.29087569 FALSE FALSE FALSE
## H.nwrds.log 0.32149418 FALSE FALSE FALSE
## H.nchrs.log 1.57685242 FALSE FALSE FALSE
## S.nwrds.log 0.73484385 FALSE FALSE FALSE
## A.nwrds.log 0.93386405 FALSE FALSE FALSE
## H.nwrds.unq.log 0.21432945 FALSE FALSE FALSE
## A.nchrs.log 4.39375383 FALSE FALSE FALSE
## S.nchrs.log 3.72014697 FALSE FALSE FALSE
## A.nwrds.unq.log 0.55113288 FALSE FALSE FALSE
## S.nwrds.unq.log 0.44396816 FALSE FALSE FALSE
## S.nuppr.log 0.33680343 FALSE FALSE FALSE
## A.nuppr.log 0.33680343 FALSE FALSE FALSE
## A.npnct05.log 0.01530925 TRUE TRUE TRUE
## A.npnct09.log 0.01530925 TRUE TRUE TRUE
## A.npnct22.log 0.01530925 TRUE TRUE TRUE
## A.npnct26.log 0.01530925 TRUE TRUE TRUE
## A.npnct27.log 0.01530925 TRUE TRUE TRUE
## A.npnct29.log 0.01530925 TRUE TRUE TRUE
## A.npnct30.log 0.01530925 TRUE TRUE TRUE
## H.npnct09.log 0.01530925 TRUE TRUE TRUE
## H.npnct17.log 0.01530925 TRUE TRUE TRUE
## H.npnct18.log 0.01530925 TRUE TRUE TRUE
## H.npnct21.log 0.01530925 TRUE TRUE TRUE
## H.npnct22.log 0.01530925 TRUE TRUE TRUE
## H.npnct23.log 0.01530925 TRUE TRUE TRUE
## H.npnct25.log 0.01530925 TRUE TRUE TRUE
## H.npnct26.log 0.01530925 TRUE TRUE TRUE
## H.npnct27.log 0.01530925 TRUE TRUE TRUE
## H.npnct29.log 0.01530925 TRUE TRUE TRUE
## H.npnct30.log 0.01530925 TRUE TRUE TRUE
## H.P.http 0.01530925 TRUE TRUE TRUE
## PubDate.year.fctr 0.01530925 TRUE TRUE TRUE
## S.npnct05.log 0.01530925 TRUE TRUE TRUE
## S.npnct09.log 0.01530925 TRUE TRUE TRUE
## S.npnct17.log 0.01530925 TRUE TRUE TRUE
## S.npnct18.log 0.01530925 TRUE TRUE TRUE
## S.npnct22.log 0.01530925 TRUE TRUE TRUE
## S.npnct25.log 0.01530925 TRUE TRUE TRUE
## S.npnct26.log 0.01530925 TRUE TRUE TRUE
## S.npnct27.log 0.01530925 TRUE TRUE TRUE
## S.npnct29.log 0.01530925 TRUE TRUE TRUE
## S.npnct30.log 0.01530925 TRUE TRUE TRUE
## S.P.http 0.01530925 TRUE TRUE TRUE
## is.cor.y.abs.low
## Popular FALSE
## WordCount.log FALSE
## A.ratio.sum.TfIdf.nwrds FALSE
## S.ratio.sum.TfIdf.nwrds FALSE
## WordCount FALSE
## H.ratio.sum.TfIdf.nwrds FALSE
## myCategory.fctr FALSE
## PubDate.day.minutes.poly.1 FALSE
## H.sum.TfIdf FALSE
## S.sum.TfIdf FALSE
## A.sum.TfIdf FALSE
## PubDate.hour.fctr FALSE
## H.npnct19.log FALSE
## A.ratio.nstopwrds.nwrds FALSE
## S.ratio.nstopwrds.nwrds FALSE
## PubDate.wkend FALSE
## H.P.recap.colon FALSE
## H.P.quandary FALSE
## PubDate.day.minutes.poly.4 FALSE
## PubDate.day.minutes.poly.2 FALSE
## H.P.no.comment.colon FALSE
## S.npnct19.log FALSE
## A.npnct19.log FALSE
## H.P.facts.figures FALSE
## PubDate.last10 FALSE
## H.npnct08.log FALSE
## PubDate.last10.log FALSE
## PubDate.last1.log FALSE
## H.P.readers.respond FALSE
## A.T.make FALSE
## S.T.make FALSE
## H.ratio.nstopwrds.nwrds FALSE
## PubDate.last100 FALSE
## PubDate.last1 FALSE
## H.T.get FALSE
## H.npnct06.log FALSE
## A.npnct01.log FALSE
## S.npnct01.log FALSE
## A.T.can FALSE
## H.npnct16.log FALSE
## S.T.can FALSE
## PubDate.day.minutes.poly.3 FALSE
## S.npnct21.log FALSE
## S.npnct23.log FALSE
## H.T.ebola FALSE
## H.npnct01.log FALSE
## PubDate.month.fctr FALSE
## A.T.said FALSE
## S.T.said FALSE
## PubDate.POSIX FALSE
## PubDate.zoo FALSE
## A.npnct21.log FALSE
## A.npnct23.log FALSE
## H.T.make FALSE
## H.npnct11.log FALSE
## UniqueID FALSE
## A.T.one FALSE
## S.T.one FALSE
## H.npnct03.log FALSE
## H.P.s.notebook TRUE
## A.npnct24.log TRUE
## H.npnct24.log TRUE
## S.npnct24.log TRUE
## H.T.take TRUE
## A.npnct16.log TRUE
## S.npnct16.log TRUE
## A.T.presid TRUE
## S.T.presid TRUE
## S.npnct08.log TRUE
## A.npnct08.log TRUE
## A.npnct25.log TRUE
## A.npnct10.log TRUE
## H.npnct10.log TRUE
## H.npnct20.log TRUE
## S.npnct02.log TRUE
## S.npnct10.log TRUE
## PubDate.last100.log TRUE
## .rnorm FALSE
## H.npnct05.log FALSE
## H.P.friday.night.music FALSE
## H.T.say FALSE
## H.T.obama FALSE
## H.T.bank FALSE
## PubDate.date.fctr FALSE
## PubDate.second.fctr FALSE
## H.npnct07.log FALSE
## A.npnct07.log FALSE
## S.npnct07.log FALSE
## S.npnct03.log FALSE
## A.npnct18.log FALSE
## A.P.http FALSE
## H.npnct12.log FALSE
## A.npnct03.log FALSE
## H.T.word FALSE
## H.T.big FALSE
## A.npnct02.log FALSE
## A.npnct17.log FALSE
## A.P.year.colon FALSE
## S.P.year.colon FALSE
## S.T.obama FALSE
## A.T.obama FALSE
## A.npnct20.log FALSE
## S.npnct20.log FALSE
## H.npnct02.log FALSE
## H.T.test FALSE
## S.npnct14.log FALSE
## A.P.first.draft FALSE
## H.P.on.this.day FALSE
## S.P.first.draft FALSE
## S.T.take FALSE
## A.T.take FALSE
## A.npnct06.log FALSE
## S.npnct06.log FALSE
## A.npnct14.log FALSE
## S.T.time FALSE
## A.T.time FALSE
## H.T.newyorktim FALSE
## H.npnct13.log FALSE
## H.T.deal FALSE
## S.T.new FALSE
## A.T.new FALSE
## H.T.billion FALSE
## A.P.metropolitan.diary.colon FALSE
## S.P.metropolitan.diary.colon FALSE
## H.T.polit FALSE
## H.P.verbatim.colon FALSE
## H.T.china FALSE
## H.T.art FALSE
## PubDate.minute.fctr FALSE
## H.T.read FALSE
## S.npnct12.log FALSE
## H.P.today.in.politic FALSE
## A.T.year FALSE
## S.T.year FALSE
## A.npnct12.log FALSE
## H.P.what.we.are FALSE
## A.T.will FALSE
## S.T.will FALSE
## A.T.appear FALSE
## S.T.appear FALSE
## PubDate.wkday.fctr FALSE
## H.T.pictur FALSE
## H.T.new FALSE
## A.T.senat FALSE
## S.T.senat FALSE
## S.T.show FALSE
## A.T.show FALSE
## H.P.today.in.smallbusiness FALSE
## S.T.day FALSE
## A.T.day FALSE
## H.P.first.draft FALSE
## S.npnct28.log FALSE
## A.npnct28.log FALSE
## A.P.daily.clip.report FALSE
## H.P.daily.clip.report FALSE
## H.T.clip FALSE
## S.P.daily.clip.report FALSE
## A.T.first FALSE
## H.T.news FALSE
## S.T.first FALSE
## H.T.first FALSE
## H.T.X2014 FALSE
## A.T.newyork FALSE
## S.T.newyork FALSE
## A.T.report FALSE
## A.T.compani FALSE
## S.T.report FALSE
## S.T.compani FALSE
## A.T.word FALSE
## S.T.word FALSE
## H.T.morn FALSE
## H.T.busi FALSE
## A.T.newyorktim FALSE
## S.T.newyorktim FALSE
## A.npnct13.log FALSE
## A.T.share FALSE
## S.T.share FALSE
## H.npnct04.log FALSE
## S.npnct13.log FALSE
## A.T.articl FALSE
## S.T.articl FALSE
## H.T.newyork FALSE
## PubDate.day.minutes.poly.5 FALSE
## H.T.today FALSE
## H.T.springsumm FALSE
## H.T.day FALSE
## H.npnct14.log FALSE
## A.T.diari FALSE
## S.T.diari FALSE
## H.T.report FALSE
## A.npnct04.log FALSE
## S.npnct04.log FALSE
## H.T.daili FALSE
## H.T.X2015 FALSE
## A.T.herald FALSE
## S.T.herald FALSE
## S.npnct15.log FALSE
## H.T.week FALSE
## A.T.photo FALSE
## S.T.photo FALSE
## A.npnct15.log FALSE
## A.T.intern FALSE
## S.T.intern FALSE
## A.T.tribun FALSE
## S.T.tribun FALSE
## A.P.fashion.week FALSE
## S.P.fashion.week FALSE
## A.T.archiv FALSE
## S.T.archiv FALSE
## H.P.fashion.week FALSE
## H.P.year.colon FALSE
## H.T.fashion FALSE
## H.npnct15.log FALSE
## A.T.fashion FALSE
## S.T.fashion FALSE
## A.T.week FALSE
## S.T.week FALSE
## H.nstopwrds.log FALSE
## .clusterid FALSE
## .clusterid.fctr FALSE
## H.npnct28.log FALSE
## S.npnct11.log FALSE
## A.npnct11.log FALSE
## S.nstopwrds.log FALSE
## A.nstopwrds.log FALSE
## H.ndgts.log FALSE
## S.ndgts.log FALSE
## A.ndgts.log FALSE
## H.nuppr.log FALSE
## H.nwrds.log FALSE
## H.nchrs.log FALSE
## S.nwrds.log FALSE
## A.nwrds.log FALSE
## H.nwrds.unq.log FALSE
## A.nchrs.log FALSE
## S.nchrs.log FALSE
## A.nwrds.unq.log FALSE
## S.nwrds.unq.log FALSE
## S.nuppr.log FALSE
## A.nuppr.log FALSE
## A.npnct05.log NA
## A.npnct09.log NA
## A.npnct22.log NA
## A.npnct26.log NA
## A.npnct27.log NA
## A.npnct29.log NA
## A.npnct30.log NA
## H.npnct09.log NA
## H.npnct17.log NA
## H.npnct18.log NA
## H.npnct21.log NA
## H.npnct22.log NA
## H.npnct23.log NA
## H.npnct25.log NA
## H.npnct26.log NA
## H.npnct27.log NA
## H.npnct29.log NA
## H.npnct30.log NA
## H.P.http NA
## PubDate.year.fctr NA
## S.npnct05.log NA
## S.npnct09.log NA
## S.npnct17.log NA
## S.npnct18.log NA
## S.npnct22.log NA
## S.npnct25.log NA
## S.npnct26.log NA
## S.npnct27.log NA
## S.npnct29.log NA
## S.npnct30.log NA
## S.P.http NA#subset(glb_feats_df, id %in% c("A.nuppr.log", "S.nuppr.log"))
print(myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
colorcol_name="myNearZV", jitter=TRUE) +
geom_point(aes(shape=nzv)) + xlim(-5, 25))## Warning in myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "myNearZV", : converting myNearZV to class:factor## Warning in loop_apply(n, do.ply): Removed 16 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 16 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 16 rows containing missing values
## (geom_point).
print(subset(glb_feats_df, myNearZV))## id cor.y exclude.as.feat
## S.npnct21.log S.npnct21.log 2.760321e-02 0
## S.npnct23.log S.npnct23.log 2.760321e-02 0
## A.npnct21.log A.npnct21.log 1.537569e-02 0
## A.npnct23.log A.npnct23.log 1.537569e-02 0
## H.npnct03.log H.npnct03.log 9.533020e-03 0
## A.npnct24.log A.npnct24.log -9.890046e-19 0
## H.npnct24.log H.npnct24.log -9.890046e-19 0
## S.npnct24.log S.npnct24.log -9.890046e-19 0
## A.npnct25.log A.npnct25.log -5.547032e-03 0
## A.npnct10.log A.npnct10.log -5.547032e-03 0
## H.npnct10.log H.npnct10.log -5.547032e-03 0
## H.npnct20.log H.npnct20.log -5.547032e-03 0
## S.npnct02.log S.npnct02.log -5.547032e-03 0
## S.npnct10.log S.npnct10.log -5.547032e-03 0
## A.npnct05.log A.npnct05.log NA 0
## A.npnct09.log A.npnct09.log NA 0
## A.npnct22.log A.npnct22.log NA 0
## A.npnct26.log A.npnct26.log NA 0
## A.npnct27.log A.npnct27.log NA 0
## A.npnct29.log A.npnct29.log NA 0
## A.npnct30.log A.npnct30.log NA 0
## H.npnct09.log H.npnct09.log NA 0
## H.npnct17.log H.npnct17.log NA 0
## H.npnct18.log H.npnct18.log NA 0
## H.npnct21.log H.npnct21.log NA 0
## H.npnct22.log H.npnct22.log NA 0
## H.npnct23.log H.npnct23.log NA 0
## H.npnct25.log H.npnct25.log NA 0
## H.npnct26.log H.npnct26.log NA 0
## H.npnct27.log H.npnct27.log NA 0
## H.npnct29.log H.npnct29.log NA 0
## H.npnct30.log H.npnct30.log NA 0
## H.P.http H.P.http NA 0
## PubDate.year.fctr PubDate.year.fctr NA 0
## S.npnct05.log S.npnct05.log NA 0
## S.npnct09.log S.npnct09.log NA 0
## S.npnct17.log S.npnct17.log NA 0
## S.npnct18.log S.npnct18.log NA 0
## S.npnct22.log S.npnct22.log NA 0
## S.npnct25.log S.npnct25.log NA 0
## S.npnct26.log S.npnct26.log NA 0
## S.npnct27.log S.npnct27.log NA 0
## S.npnct29.log S.npnct29.log NA 0
## S.npnct30.log S.npnct30.log NA 0
## S.P.http S.P.http NA 0
## cor.y.abs cor.high.X freqRatio percentUnique
## S.npnct21.log 2.760321e-02 <NA> 6531.000 0.03061849
## S.npnct23.log 2.760321e-02 S.npnct21.log 6531.000 0.03061849
## A.npnct21.log 1.537569e-02 S.npnct21.log 3264.500 0.04592774
## A.npnct23.log 1.537569e-02 A.npnct21.log 3264.500 0.04592774
## H.npnct03.log 9.533020e-03 <NA> 2176.333 0.03061849
## A.npnct24.log 9.890046e-19 <NA> 0.000 0.01530925
## H.npnct24.log 9.890046e-19 <NA> 0.000 0.01530925
## S.npnct24.log 9.890046e-19 <NA> 0.000 0.01530925
## A.npnct25.log 5.547032e-03 <NA> 6531.000 0.03061849
## A.npnct10.log 5.547032e-03 <NA> 6531.000 0.03061849
## H.npnct10.log 5.547032e-03 <NA> 6531.000 0.03061849
## H.npnct20.log 5.547032e-03 <NA> 6531.000 0.03061849
## S.npnct02.log 5.547032e-03 <NA> 6531.000 0.03061849
## S.npnct10.log 5.547032e-03 <NA> 6531.000 0.03061849
## A.npnct05.log NA <NA> 0.000 0.01530925
## A.npnct09.log NA <NA> 0.000 0.01530925
## A.npnct22.log NA <NA> 0.000 0.01530925
## A.npnct26.log NA <NA> 0.000 0.01530925
## A.npnct27.log NA <NA> 0.000 0.01530925
## A.npnct29.log NA <NA> 0.000 0.01530925
## A.npnct30.log NA <NA> 0.000 0.01530925
## H.npnct09.log NA <NA> 0.000 0.01530925
## H.npnct17.log NA <NA> 0.000 0.01530925
## H.npnct18.log NA <NA> 0.000 0.01530925
## H.npnct21.log NA <NA> 0.000 0.01530925
## H.npnct22.log NA <NA> 0.000 0.01530925
## H.npnct23.log NA <NA> 0.000 0.01530925
## H.npnct25.log NA <NA> 0.000 0.01530925
## H.npnct26.log NA <NA> 0.000 0.01530925
## H.npnct27.log NA <NA> 0.000 0.01530925
## H.npnct29.log NA <NA> 0.000 0.01530925
## H.npnct30.log NA <NA> 0.000 0.01530925
## H.P.http NA <NA> 0.000 0.01530925
## PubDate.year.fctr NA <NA> 0.000 0.01530925
## S.npnct05.log NA <NA> 0.000 0.01530925
## S.npnct09.log NA <NA> 0.000 0.01530925
## S.npnct17.log NA <NA> 0.000 0.01530925
## S.npnct18.log NA <NA> 0.000 0.01530925
## S.npnct22.log NA <NA> 0.000 0.01530925
## S.npnct25.log NA <NA> 0.000 0.01530925
## S.npnct26.log NA <NA> 0.000 0.01530925
## S.npnct27.log NA <NA> 0.000 0.01530925
## S.npnct29.log NA <NA> 0.000 0.01530925
## S.npnct30.log NA <NA> 0.000 0.01530925
## S.P.http NA <NA> 0.000 0.01530925
## zeroVar nzv myNearZV is.cor.y.abs.low
## S.npnct21.log FALSE TRUE TRUE FALSE
## S.npnct23.log FALSE TRUE TRUE FALSE
## A.npnct21.log FALSE TRUE TRUE FALSE
## A.npnct23.log FALSE TRUE TRUE FALSE
## H.npnct03.log FALSE TRUE TRUE FALSE
## A.npnct24.log TRUE TRUE TRUE TRUE
## H.npnct24.log TRUE TRUE TRUE TRUE
## S.npnct24.log TRUE TRUE TRUE TRUE
## A.npnct25.log FALSE TRUE TRUE TRUE
## A.npnct10.log FALSE TRUE TRUE TRUE
## H.npnct10.log FALSE TRUE TRUE TRUE
## H.npnct20.log FALSE TRUE TRUE TRUE
## S.npnct02.log FALSE TRUE TRUE TRUE
## S.npnct10.log FALSE TRUE TRUE TRUE
## A.npnct05.log TRUE TRUE TRUE NA
## A.npnct09.log TRUE TRUE TRUE NA
## A.npnct22.log TRUE TRUE TRUE NA
## A.npnct26.log TRUE TRUE TRUE NA
## A.npnct27.log TRUE TRUE TRUE NA
## A.npnct29.log TRUE TRUE TRUE NA
## A.npnct30.log TRUE TRUE TRUE NA
## H.npnct09.log TRUE TRUE TRUE NA
## H.npnct17.log TRUE TRUE TRUE NA
## H.npnct18.log TRUE TRUE TRUE NA
## H.npnct21.log TRUE TRUE TRUE NA
## H.npnct22.log TRUE TRUE TRUE NA
## H.npnct23.log TRUE TRUE TRUE NA
## H.npnct25.log TRUE TRUE TRUE NA
## H.npnct26.log TRUE TRUE TRUE NA
## H.npnct27.log TRUE TRUE TRUE NA
## H.npnct29.log TRUE TRUE TRUE NA
## H.npnct30.log TRUE TRUE TRUE NA
## H.P.http TRUE TRUE TRUE NA
## PubDate.year.fctr TRUE TRUE TRUE NA
## S.npnct05.log TRUE TRUE TRUE NA
## S.npnct09.log TRUE TRUE TRUE NA
## S.npnct17.log TRUE TRUE TRUE NA
## S.npnct18.log TRUE TRUE TRUE NA
## S.npnct22.log TRUE TRUE TRUE NA
## S.npnct25.log TRUE TRUE TRUE NA
## S.npnct26.log TRUE TRUE TRUE NA
## S.npnct27.log TRUE TRUE TRUE NA
## S.npnct29.log TRUE TRUE TRUE NA
## S.npnct30.log TRUE TRUE TRUE NA
## S.P.http TRUE TRUE TRUE NAglb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
subset(glb_feats_df, myNearZV)$id)]
if (!is.null(glb_interaction_only_features))
glb_feats_df[glb_feats_df$id %in% glb_interaction_only_features, "interaction.feat"] <-
names(glb_interaction_only_features)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 8 select.features 5 0 278.879 419.776 140.897
## 9 partition.data.training 6 0 419.777 NA NAStep 6.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=1 - (nrow(glb_newobs_df) * 1.1 / nrow(glb_trnobs_df)))
glb_fitobs_df <- glb_trnobs_df[split, ]
glb_OOBobs_df <- glb_trnobs_df[!split ,]
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## Loading required package: caToolsif (!is.null(glb_max_fitent_obs) && (nrow(glb_fitobs_df) > glb_max_fitent_obs)) {
warning("glb_fitobs_df restricted to glb_max_fitent_obs: ",
format(glb_max_fitent_obs, big.mark=","))
org_fitent_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitent_df[split <- sample.split(org_fitent_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitent_obs), ]
org_fitent_df <- NULL
}
glb_allobs_df$.lcn <- ""
glb_allobs_df[glb_allobs_df[, glb_id_vars] %in%
glb_fitobs_df[, glb_id_vars], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_vars] %in%
glb_OOBobs_df[, glb_id_vars], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)## Popular.0 Popular.1 Popular.NA
## NA NA 1870
## Fit 3726 749 NA
## OOB 1713 344 NA
## Popular.0 Popular.1 Popular.NA
## NA NA 1
## Fit 0.8326257 0.1673743 NA
## OOB 0.8327662 0.1672338 NAnewent_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
"myCategory")
OOBent_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
"myCategory")
glb_ctgry_df <- merge(newent_ctgry_df, OOBent_ctgry_df, by="myCategory", all=TRUE,
suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))## myCategory .n.Tst .n.OOB .freqRatio.Tst
## 1 ## 338 407 0.180748663
## 6 Business#Business Day#Dealbook 304 312 0.162566845
## 10 Culture#Arts# 244 225 0.130481283
## 15 OpEd#Opinion# 164 154 0.087700535
## 9 Business#Technology# 113 114 0.060427807
## 20 TStyle## 105 221 0.056149733
## 5 #U.S.#Education 90 93 0.048128342
## 13 Metro#N.Y. / Region# 67 60 0.035828877
## 18 Styles#U.S.# 62 54 0.033155080
## 16 Science#Health# 57 66 0.030481283
## 12 Foreign#World#Asia Pacific 56 61 0.029946524
## 2 #Multimedia# 52 42 0.027807487
## 11 Foreign#World# 47 47 0.025133690
## 7 Business#Business Day#Small Business 42 45 0.022459893
## 8 Business#Crosswords/Games# 42 40 0.022459893
## 19 Travel#Travel# 35 31 0.018716578
## 3 #Opinion#Room For Debate 24 21 0.012834225
## 17 Styles##Fashion 15 41 0.008021390
## 4 #Opinion#The Public Editor 10 10 0.005347594
## 14 myOther 3 13 0.001604278
## .freqRatio.OOB
## 1 0.197860963
## 6 0.151677200
## 10 0.109382596
## 15 0.074866310
## 9 0.055420515
## 20 0.107438017
## 5 0.045211473
## 13 0.029168692
## 18 0.026251823
## 16 0.032085561
## 12 0.029654837
## 2 0.020418085
## 11 0.022848809
## 7 0.021876519
## 8 0.019445795
## 19 0.015070491
## 3 0.010209042
## 17 0.019931940
## 4 0.004861449
## 14 0.006319883# Run this line by line
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 271 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_vars) && glb_id_vars != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_vars, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## Popular.fctr Popular.fctr TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var))## id cor.y exclude.as.feat cor.y.abs cor.high.X
## Popular Popular 1.00000000 TRUE 1.00000000 <NA>
## UniqueID UniqueID 0.01182492 TRUE 0.01182492 <NA>
## Popular.fctr Popular.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV
## Popular 4.976212 0.03061849 FALSE FALSE FALSE
## UniqueID 1.000000 100.00000000 FALSE FALSE FALSE
## Popular.fctr NA NA NA NA NA
## is.cor.y.abs.low interaction.feat rsp_var_raw id_var rsp_var
## Popular FALSE <NA> TRUE NA NA
## UniqueID FALSE <NA> FALSE TRUE NA
## Popular.fctr NA <NA> NA NA TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## [1] "S.npnct21.log" "S.npnct23.log" "A.npnct21.log"
## [4] "A.npnct23.log" "H.npnct03.log" "A.npnct24.log"
## [7] "H.npnct24.log" "S.npnct24.log" "A.npnct25.log"
## [10] "A.npnct10.log" "H.npnct10.log" "H.npnct20.log"
## [13] "S.npnct02.log" "S.npnct10.log" "A.npnct05.log"
## [16] "A.npnct09.log" "A.npnct22.log" "A.npnct26.log"
## [19] "A.npnct27.log" "A.npnct29.log" "A.npnct30.log"
## [22] "H.npnct09.log" "H.npnct17.log" "H.npnct18.log"
## [25] "H.npnct21.log" "H.npnct22.log" "H.npnct23.log"
## [28] "H.npnct25.log" "H.npnct26.log" "H.npnct27.log"
## [31] "H.npnct29.log" "H.npnct30.log" "H.P.http"
## [34] "PubDate.year.fctr" "S.npnct05.log" "S.npnct09.log"
## [37] "S.npnct17.log" "S.npnct18.log" "S.npnct22.log"
## [40] "S.npnct25.log" "S.npnct26.log" "S.npnct27.log"
## [43] "S.npnct29.log" "S.npnct30.log" "S.P.http"print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)#print(setdiff(setdiff(names(glb_allobs_df), glb_exclude_vars_as_features),
# glb_feats_df$id))
print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 8402 237print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 6532 281print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 4475 281print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 2057 281print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 1870 281# # Does not handle NULL or length(glb_id_vars) > 1
# glb_allobs_df$.src.trn <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_vars] %in% glb_trnobs_df[, glb_id_vars],
# ".src.trn"] <- 1
# glb_allobs_df$.src.fit <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_vars] %in% glb_fitobs_df[, glb_id_vars],
# ".src.fit"] <- 1
# glb_allobs_df$.src.OOB <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_vars] %in% glb_OOBobs_df[, glb_id_vars],
# ".src.OOB"] <- 1
# glb_allobs_df$.src.new <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_vars] %in% glb_newobs_df[, glb_id_vars],
# ".src.new"] <- 1
# #print(unique(glb_allobs_df[, ".src.trn"]))
# write_cols <- c(glb_feats_df$id,
# ".src.trn", ".src.fit", ".src.OOB", ".src.new")
# glb_allobs_df <- glb_allobs_df[, write_cols]
#
# tmp_feats_df <- glb_feats_df
# tmp_entity_df <- glb_allobs_df
if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
rm(split)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 partition.data.training 6 0 419.777 420.865 1.088
## 10 fit.models 7 0 420.866 NA NAStep 7.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
# keep_cols <- setdiff(names(glb_allobs_df),
# grep("^.src", names(glb_allobs_df), value=TRUE))
# glb_trnobs_df <- glb_allobs_df[glb_allobs_df$.src.trn == 1, keep_cols]
# glb_fitobs_df <- glb_allobs_df[glb_allobs_df$.src.fit == 1, keep_cols]
# glb_OOBobs_df <- glb_allobs_df[glb_allobs_df$.src.OOB == 1, keep_cols]
# glb_newobs_df <- glb_allobs_df[glb_allobs_df$.src.new == 1, keep_cols]
#
# glb_models_lst <- list(); glb_models_df <- data.frame()
#
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a lower correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl_fn(model_id="Baseline", model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: MFO.myMFO_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
## N Y
## 0.8326257 0.1673743
## [1] "MFO.val:"
## [1] "N"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"## Loading required package: ROCR
## Loading required package: gplots
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8326257 0.1673743
## 2 0.8326257 0.1673743
## 3 0.8326257 0.1673743
## 4 0.8326257 0.1673743
## 5 0.8326257 0.1673743
## 6 0.8326257 0.1673743
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.MFO.myMFO_classfr.N
## 1 N 3726
## 2 Y 749
## Prediction
## Reference N Y
## N 3726 0
## Y 749 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.326257e-01 0.000000e+00 8.213602e-01 8.434553e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 5.097571e-01 1.800616e-164
## [1] " calling mypredict_mdl for OOB:"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8326257 0.1673743
## 2 0.8326257 0.1673743
## 3 0.8326257 0.1673743
## 4 0.8326257 0.1673743
## 5 0.8326257 0.1673743
## 6 0.8326257 0.1673743
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.MFO.myMFO_classfr.N
## 1 N 1713
## 2 Y 344
## Prediction
## Reference N Y
## N 1713 0
## Y 344 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.327662e-01 0.000000e+00 8.159247e-01 8.486533e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 5.143944e-01 2.337097e-76
## model_id model_method feats max.nTuningRuns
## 1 MFO.myMFO_classfr myMFO_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.94 0.003 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.8326257
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8213602 0.8434553 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.8327662
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8159247 0.8486533 0if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Random.myrandom_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.2867534
## 3 0.2 0.1746905
## 4 0.3 0.1746905
## 5 0.4 0.1746905
## 6 0.5 0.1746905
## 7 0.6 0.1746905
## 8 0.7 0.1746905
## 9 0.8 0.1746905
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.1000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Random.myrandom_classfr.Y
## 1 N 3726
## 2 Y 749
## Prediction
## Reference N Y
## N 0 3726
## Y 0 749
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.1673743 0.0000000 0.1565447 0.1786398 0.8326257
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## [1] " calling mypredict_mdl for OOB:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.2865473
## 3 0.2 0.1553398
## 4 0.3 0.1553398
## 5 0.4 0.1553398
## 6 0.5 0.1553398
## 7 0.6 0.1553398
## 8 0.7 0.1553398
## 9 0.8 0.1553398
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.1000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Random.myrandom_classfr.Y
## 1 N 1713
## 2 Y 344
## Prediction
## Reference N Y
## N 0 1713
## Y 0 344
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.1672338 0.0000000 0.1513467 0.1840753 0.8327662
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## model_id model_method feats max.nTuningRuns
## 1 Random.myrandom_classfr myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.362 0.001 0.5072166
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.1 0.2867534 0.1673743
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.1565447 0.1786398 0 0.4877001
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.2865473 0.1672338
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.1513467 0.1840753 0# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: A.nuppr.log, WordCount.log"## Loading required package: rpart## Fitting cp = 0.00324 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4475
##
## CP nsplit rel error
## 1 0.003242418 0 1
##
## Node number 1: 4475 observations
## predicted class=N expected loss=0.1673743 P(node) =1
## class counts: 3726 749
## probabilities: 0.833 0.167
##
## n= 4475
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4475 749 N (0.8326257 0.1673743) *
## [1] " calling mypredict_mdl for fit:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 3726
## 2 Y 749
## Prediction
## Reference N Y
## N 3726 0
## Y 749 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.326257e-01 0.000000e+00 8.213602e-01 8.434553e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 5.097571e-01 1.800616e-164
## [1] " calling mypredict_mdl for OOB:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 1713
## 2 Y 344
## Prediction
## Reference N Y
## N 1713 0
## Y 344 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.327662e-01 0.000000e+00 8.159247e-01 8.486533e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 5.143944e-01 2.337097e-76
## model_id model_method feats
## 1 Max.cor.Y.cv.0.rpart rpart A.nuppr.log, WordCount.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.882 0.085
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0 0.8326257
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8213602 0.8434553 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.8327662
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8159247 0.8486533 0ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: A.nuppr.log, WordCount.log"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4475
##
## CP nsplit rel error
## 1 0.0032424185 0 1.0000000
## 2 0.0026702270 14 0.9385848
## 3 0.0020026702 18 0.9279039
## 4 0.0016021362 30 0.9025367
## 5 0.0013351135 37 0.8891856
## 6 0.0008900757 61 0.8544726
## 7 0.0006675567 67 0.8491322
## 8 0.0005340454 71 0.8464619
## 9 0.0004450378 78 0.8424566
## 10 0.0000000000 92 0.8331108
##
## Variable importance
## WordCount.log A.nuppr.log
## 72 28
##
## Node number 1: 4475 observations, complexity param=0.003242418
## predicted class=N expected loss=0.1673743 P(node) =1
## class counts: 3726 749
## probabilities: 0.833 0.167
## left son=2 (3266 obs) right son=3 (1209 obs)
## Primary splits:
## WordCount.log < 6.524296 to the left, improve=106.38390, (0 missing)
## A.nuppr.log < 1.497866 to the right, improve= 86.35796, (0 missing)
##
## Node number 2: 3266 observations, complexity param=0.001602136
## predicted class=N expected loss=0.101041 P(node) =0.7298324
## class counts: 2936 330
## probabilities: 0.899 0.101
## left son=4 (2468 obs) right son=5 (798 obs)
## Primary splits:
## A.nuppr.log < 1.242453 to the right, improve=40.40121, (0 missing)
## WordCount.log < 5.791487 to the left, improve=17.31572, (0 missing)
##
## Node number 3: 1209 observations, complexity param=0.003242418
## predicted class=N expected loss=0.3465674 P(node) =0.2701676
## class counts: 790 419
## probabilities: 0.653 0.347
## left son=6 (560 obs) right son=7 (649 obs)
## Primary splits:
## A.nuppr.log < 1.497866 to the right, improve=21.674910, (0 missing)
## WordCount.log < 7.602894 to the right, improve= 3.403155, (0 missing)
## Surrogate splits:
## WordCount.log < 7.272745 to the right, agree=0.549, adj=0.027, (0 split)
##
## Node number 4: 2468 observations, complexity param=0.0005340454
## predicted class=N expected loss=0.05632091 P(node) =0.5515084
## class counts: 2329 139
## probabilities: 0.944 0.056
## left son=8 (1836 obs) right son=9 (632 obs)
## Primary splits:
## WordCount.log < 6.019808 to the left, improve=9.967109, (0 missing)
## A.nuppr.log < 1.497866 to the right, improve=5.274466, (0 missing)
##
## Node number 5: 798 observations, complexity param=0.001602136
## predicted class=N expected loss=0.2393484 P(node) =0.178324
## class counts: 607 191
## probabilities: 0.761 0.239
## left son=10 (429 obs) right son=11 (369 obs)
## Primary splits:
## WordCount.log < 5.736551 to the left, improve=10.118870, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve= 1.042481, (0 missing)
## Surrogate splits:
## A.nuppr.log < 0.8958797 to the left, agree=0.566, adj=0.062, (0 split)
##
## Node number 6: 560 observations, complexity param=0.001335113
## predicted class=N expected loss=0.2446429 P(node) =0.1251397
## class counts: 423 137
## probabilities: 0.755 0.245
## left son=12 (93 obs) right son=13 (467 obs)
## Primary splits:
## A.nuppr.log < 2.249905 to the right, improve=4.193341, (0 missing)
## WordCount.log < 7.602894 to the right, improve=2.183960, (0 missing)
##
## Node number 7: 649 observations, complexity param=0.003242418
## predicted class=N expected loss=0.4345146 P(node) =0.1450279
## class counts: 367 282
## probabilities: 0.565 0.435
## left son=14 (194 obs) right son=15 (455 obs)
## Primary splits:
## A.nuppr.log < 1.242453 to the right, improve=1.558781, (0 missing)
## WordCount.log < 7.132098 to the left, improve=1.301530, (0 missing)
## Surrogate splits:
## WordCount.log < 6.527226 to the left, agree=0.703, adj=0.005, (0 split)
##
## Node number 8: 1836 observations
## predicted class=N expected loss=0.02995643 P(node) =0.4102793
## class counts: 1781 55
## probabilities: 0.970 0.030
##
## Node number 9: 632 observations, complexity param=0.0005340454
## predicted class=N expected loss=0.1329114 P(node) =0.1412291
## class counts: 548 84
## probabilities: 0.867 0.133
## left son=18 (486 obs) right son=19 (146 obs)
## Primary splits:
## A.nuppr.log < 1.497866 to the right, improve=3.7945680, (0 missing)
## WordCount.log < 6.034284 to the right, improve=0.5497624, (0 missing)
##
## Node number 10: 429 observations
## predicted class=N expected loss=0.1655012 P(node) =0.09586592
## class counts: 358 71
## probabilities: 0.834 0.166
##
## Node number 11: 369 observations, complexity param=0.001602136
## predicted class=N expected loss=0.3252033 P(node) =0.0824581
## class counts: 249 120
## probabilities: 0.675 0.325
## left son=22 (223 obs) right son=23 (146 obs)
## Primary splits:
## WordCount.log < 6.085637 to the right, improve=2.054481, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve=1.961796, (0 missing)
##
## Node number 12: 93 observations
## predicted class=N expected loss=0.1075269 P(node) =0.02078212
## class counts: 83 10
## probabilities: 0.892 0.108
##
## Node number 13: 467 observations, complexity param=0.001335113
## predicted class=N expected loss=0.2719486 P(node) =0.1043575
## class counts: 340 127
## probabilities: 0.728 0.272
## left son=26 (33 obs) right son=27 (434 obs)
## Primary splits:
## WordCount.log < 7.602894 to the right, improve=1.0300670, (0 missing)
## A.nuppr.log < 1.868835 to the left, improve=0.2061214, (0 missing)
##
## Node number 14: 194 observations, complexity param=0.003242418
## predicted class=N expected loss=0.3814433 P(node) =0.04335196
## class counts: 120 74
## probabilities: 0.619 0.381
## left son=28 (181 obs) right son=29 (13 obs)
## Primary splits:
## WordCount.log < 6.588926 to the right, improve=2.693013, (0 missing)
##
## Node number 15: 455 observations, complexity param=0.003242418
## predicted class=N expected loss=0.4571429 P(node) =0.101676
## class counts: 247 208
## probabilities: 0.543 0.457
## left son=30 (72 obs) right son=31 (383 obs)
## Primary splits:
## WordCount.log < 6.656083 to the left, improve=3.2436430, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.3647769, (0 missing)
##
## Node number 18: 486 observations
## predicted class=N expected loss=0.1028807 P(node) =0.1086034
## class counts: 436 50
## probabilities: 0.897 0.103
##
## Node number 19: 146 observations, complexity param=0.0005340454
## predicted class=N expected loss=0.2328767 P(node) =0.0326257
## class counts: 112 34
## probabilities: 0.767 0.233
## left son=38 (67 obs) right son=39 (79 obs)
## Primary splits:
## WordCount.log < 6.281328 to the right, improve=1.731737, (0 missing)
##
## Node number 22: 223 observations, complexity param=0.0004450378
## predicted class=N expected loss=0.2825112 P(node) =0.0498324
## class counts: 160 63
## probabilities: 0.717 0.283
## left son=44 (8 obs) right son=45 (215 obs)
## Primary splits:
## WordCount.log < 6.102556 to the left, improve=1.324518, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve=0.132791, (0 missing)
##
## Node number 23: 146 observations, complexity param=0.001602136
## predicted class=N expected loss=0.390411 P(node) =0.0326257
## class counts: 89 57
## probabilities: 0.610 0.390
## left son=46 (70 obs) right son=47 (76 obs)
## Primary splits:
## A.nuppr.log < 0.8958797 to the right, improve=2.948038, (0 missing)
## WordCount.log < 5.971259 to the left, improve=1.169827, (0 missing)
## Surrogate splits:
## WordCount.log < 6.012491 to the right, agree=0.582, adj=0.129, (0 split)
##
## Node number 26: 33 observations
## predicted class=N expected loss=0.1515152 P(node) =0.007374302
## class counts: 28 5
## probabilities: 0.848 0.152
##
## Node number 27: 434 observations, complexity param=0.001335113
## predicted class=N expected loss=0.281106 P(node) =0.09698324
## class counts: 312 122
## probabilities: 0.719 0.281
## left son=54 (383 obs) right son=55 (51 obs)
## Primary splits:
## WordCount.log < 7.257002 to the left, improve=3.3353930, (0 missing)
## A.nuppr.log < 1.868835 to the left, improve=0.2391718, (0 missing)
##
## Node number 28: 181 observations, complexity param=0.002670227
## predicted class=N expected loss=0.359116 P(node) =0.04044693
## class counts: 116 65
## probabilities: 0.641 0.359
## left son=56 (132 obs) right son=57 (49 obs)
## Primary splits:
## WordCount.log < 7.087571 to the left, improve=1.085171, (0 missing)
##
## Node number 29: 13 observations
## predicted class=Y expected loss=0.3076923 P(node) =0.002905028
## class counts: 4 9
## probabilities: 0.308 0.692
##
## Node number 30: 72 observations, complexity param=0.0008900757
## predicted class=N expected loss=0.3194444 P(node) =0.01608939
## class counts: 49 23
## probabilities: 0.681 0.319
## left son=60 (37 obs) right son=61 (35 obs)
## Primary splits:
## A.nuppr.log < 0.8958797 to the left, improve=0.8839339, (0 missing)
## WordCount.log < 6.620739 to the right, improve=0.5918426, (0 missing)
## Surrogate splits:
## WordCount.log < 6.60055 to the left, agree=0.597, adj=0.171, (0 split)
##
## Node number 31: 383 observations, complexity param=0.003242418
## predicted class=N expected loss=0.4830287 P(node) =0.08558659
## class counts: 198 185
## probabilities: 0.517 0.483
## left son=62 (35 obs) right son=63 (348 obs)
## Primary splits:
## WordCount.log < 7.573528 to the right, improve=1.51369200, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.08300754, (0 missing)
##
## Node number 38: 67 observations
## predicted class=N expected loss=0.1492537 P(node) =0.01497207
## class counts: 57 10
## probabilities: 0.851 0.149
##
## Node number 39: 79 observations, complexity param=0.0005340454
## predicted class=N expected loss=0.3037975 P(node) =0.01765363
## class counts: 55 24
## probabilities: 0.696 0.304
## left son=78 (22 obs) right son=79 (57 obs)
## Primary splits:
## WordCount.log < 6.094697 to the left, improve=2.763814, (0 missing)
##
## Node number 44: 8 observations
## predicted class=N expected loss=0 P(node) =0.001787709
## class counts: 8 0
## probabilities: 1.000 0.000
##
## Node number 45: 215 observations, complexity param=0.0004450378
## predicted class=N expected loss=0.2930233 P(node) =0.04804469
## class counts: 152 63
## probabilities: 0.707 0.293
## left son=90 (11 obs) right son=91 (204 obs)
## Primary splits:
## WordCount.log < 6.505783 to the right, improve=0.94716250, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve=0.06816634, (0 missing)
##
## Node number 46: 70 observations
## predicted class=N expected loss=0.2857143 P(node) =0.01564246
## class counts: 50 20
## probabilities: 0.714 0.286
##
## Node number 47: 76 observations, complexity param=0.001602136
## predicted class=N expected loss=0.4868421 P(node) =0.01698324
## class counts: 39 37
## probabilities: 0.513 0.487
## left son=94 (58 obs) right son=95 (18 obs)
## Primary splits:
## WordCount.log < 5.821556 to the right, improve=1.525408, (0 missing)
##
## Node number 54: 383 observations, complexity param=0.001335113
## predicted class=N expected loss=0.2584856 P(node) =0.08558659
## class counts: 284 99
## probabilities: 0.742 0.258
## left son=108 (36 obs) right son=109 (347 obs)
## Primary splits:
## A.nuppr.log < 2.138333 to the right, improve=1.1366860, (0 missing)
## WordCount.log < 7.175489 to the right, improve=0.5941133, (0 missing)
##
## Node number 55: 51 observations, complexity param=0.001335113
## predicted class=N expected loss=0.4509804 P(node) =0.01139665
## class counts: 28 23
## probabilities: 0.549 0.451
## left son=110 (43 obs) right son=111 (8 obs)
## Primary splits:
## WordCount.log < 7.282065 to the right, improve=1.6967620, (0 missing)
## A.nuppr.log < 2.012676 to the left, improve=0.9431611, (0 missing)
##
## Node number 56: 132 observations, complexity param=0.001335113
## predicted class=N expected loss=0.3257576 P(node) =0.02949721
## class counts: 89 43
## probabilities: 0.674 0.326
## left son=112 (10 obs) right son=113 (122 obs)
## Primary splits:
## WordCount.log < 7.044469 to the right, improve=1.102881, (0 missing)
##
## Node number 57: 49 observations, complexity param=0.002670227
## predicted class=N expected loss=0.4489796 P(node) =0.01094972
## class counts: 27 22
## probabilities: 0.551 0.449
## left son=114 (35 obs) right son=115 (14 obs)
## Primary splits:
## WordCount.log < 7.170483 to the right, improve=1.473469, (0 missing)
##
## Node number 60: 37 observations
## predicted class=N expected loss=0.2432432 P(node) =0.008268156
## class counts: 28 9
## probabilities: 0.757 0.243
##
## Node number 61: 35 observations, complexity param=0.0008900757
## predicted class=N expected loss=0.4 P(node) =0.007821229
## class counts: 21 14
## probabilities: 0.600 0.400
## left son=122 (14 obs) right son=123 (21 obs)
## Primary splits:
## WordCount.log < 6.602587 to the left, improve=0.6095238, (0 missing)
##
## Node number 62: 35 observations, complexity param=0.0006675567
## predicted class=N expected loss=0.3428571 P(node) =0.007821229
## class counts: 23 12
## probabilities: 0.657 0.343
## left son=124 (8 obs) right son=125 (27 obs)
## Primary splits:
## WordCount.log < 7.647309 to the left, improve=0.98439150, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.09142857, (0 missing)
##
## Node number 63: 348 observations, complexity param=0.003242418
## predicted class=N expected loss=0.4971264 P(node) =0.07776536
## class counts: 175 173
## probabilities: 0.503 0.497
## left son=126 (324 obs) right son=127 (24 obs)
## Primary splits:
## WordCount.log < 7.416671 to the left, improve=3.296722000, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.005241885, (0 missing)
##
## Node number 78: 22 observations
## predicted class=N expected loss=0.09090909 P(node) =0.004916201
## class counts: 20 2
## probabilities: 0.909 0.091
##
## Node number 79: 57 observations, complexity param=0.0005340454
## predicted class=N expected loss=0.3859649 P(node) =0.01273743
## class counts: 35 22
## probabilities: 0.614 0.386
## left son=158 (49 obs) right son=159 (8 obs)
## Primary splits:
## WordCount.log < 6.244165 to the left, improve=1.063462, (0 missing)
##
## Node number 90: 11 observations
## predicted class=N expected loss=0.09090909 P(node) =0.002458101
## class counts: 10 1
## probabilities: 0.909 0.091
##
## Node number 91: 204 observations, complexity param=0.0004450378
## predicted class=N expected loss=0.3039216 P(node) =0.04558659
## class counts: 142 62
## probabilities: 0.696 0.304
## left son=182 (197 obs) right son=183 (7 obs)
## Primary splits:
## WordCount.log < 6.489964 to the left, improve=1.0374380, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve=0.1128219, (0 missing)
##
## Node number 94: 58 observations, complexity param=0.001602136
## predicted class=N expected loss=0.4310345 P(node) =0.01296089
## class counts: 33 25
## probabilities: 0.569 0.431
## left son=188 (38 obs) right son=189 (20 obs)
## Primary splits:
## WordCount.log < 5.971259 to the left, improve=0.8640653, (0 missing)
##
## Node number 95: 18 observations
## predicted class=Y expected loss=0.3333333 P(node) =0.004022346
## class counts: 6 12
## probabilities: 0.333 0.667
##
## Node number 108: 36 observations
## predicted class=N expected loss=0.1388889 P(node) =0.008044693
## class counts: 31 5
## probabilities: 0.861 0.139
##
## Node number 109: 347 observations, complexity param=0.001335113
## predicted class=N expected loss=0.2708934 P(node) =0.0775419
## class counts: 253 94
## probabilities: 0.729 0.271
## left son=218 (70 obs) right son=219 (277 obs)
## Primary splits:
## WordCount.log < 6.656083 to the left, improve=0.8814324, (0 missing)
## A.nuppr.log < 1.868835 to the left, improve=0.5904689, (0 missing)
##
## Node number 110: 43 observations, complexity param=0.001335113
## predicted class=N expected loss=0.3953488 P(node) =0.009608939
## class counts: 26 17
## probabilities: 0.605 0.395
## left son=220 (15 obs) right son=221 (28 obs)
## Primary splits:
## A.nuppr.log < 1.700599 to the left, improve=0.7629014, (0 missing)
## WordCount.log < 7.355313 to the left, improve=0.4990262, (0 missing)
## Surrogate splits:
## WordCount.log < 7.294717 to the left, agree=0.674, adj=0.067, (0 split)
##
## Node number 111: 8 observations
## predicted class=Y expected loss=0.25 P(node) =0.001787709
## class counts: 2 6
## probabilities: 0.250 0.750
##
## Node number 112: 10 observations
## predicted class=N expected loss=0.1 P(node) =0.002234637
## class counts: 9 1
## probabilities: 0.900 0.100
##
## Node number 113: 122 observations, complexity param=0.001335113
## predicted class=N expected loss=0.3442623 P(node) =0.02726257
## class counts: 80 42
## probabilities: 0.656 0.344
## left son=226 (112 obs) right son=227 (10 obs)
## Primary splits:
## WordCount.log < 7.003059 to the left, improve=1.424824, (0 missing)
##
## Node number 114: 35 observations
## predicted class=N expected loss=0.3714286 P(node) =0.007821229
## class counts: 22 13
## probabilities: 0.629 0.371
##
## Node number 115: 14 observations
## predicted class=Y expected loss=0.3571429 P(node) =0.003128492
## class counts: 5 9
## probabilities: 0.357 0.643
##
## Node number 122: 14 observations
## predicted class=N expected loss=0.2857143 P(node) =0.003128492
## class counts: 10 4
## probabilities: 0.714 0.286
##
## Node number 123: 21 observations, complexity param=0.0008900757
## predicted class=N expected loss=0.4761905 P(node) =0.004692737
## class counts: 11 10
## probabilities: 0.524 0.476
## left son=246 (7 obs) right son=247 (14 obs)
## Primary splits:
## WordCount.log < 6.642486 to the right, improve=0.7619048, (0 missing)
##
## Node number 124: 8 observations
## predicted class=N expected loss=0.125 P(node) =0.001787709
## class counts: 7 1
## probabilities: 0.875 0.125
##
## Node number 125: 27 observations, complexity param=0.0006675567
## predicted class=N expected loss=0.4074074 P(node) =0.00603352
## class counts: 16 11
## probabilities: 0.593 0.407
## left son=250 (20 obs) right son=251 (7 obs)
## Primary splits:
## A.nuppr.log < 0.8958797 to the left, improve=0.5084656, (0 missing)
## WordCount.log < 7.720902 to the right, improve=0.5084656, (0 missing)
## Surrogate splits:
## WordCount.log < 7.691636 to the right, agree=0.778, adj=0.143, (0 split)
##
## Node number 126: 324 observations, complexity param=0.003242418
## predicted class=N expected loss=0.4783951 P(node) =0.07240223
## class counts: 169 155
## probabilities: 0.522 0.478
## left son=252 (9 obs) right son=253 (315 obs)
## Primary splits:
## WordCount.log < 7.374931 to the right, improve=1.2149910, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.1389228, (0 missing)
##
## Node number 127: 24 observations
## predicted class=Y expected loss=0.25 P(node) =0.005363128
## class counts: 6 18
## probabilities: 0.250 0.750
##
## Node number 158: 49 observations, complexity param=0.0005340454
## predicted class=N expected loss=0.3469388 P(node) =0.01094972
## class counts: 32 17
## probabilities: 0.653 0.347
## left son=316 (7 obs) right son=317 (42 obs)
## Primary splits:
## WordCount.log < 6.233429 to the right, improve=1.965986, (0 missing)
##
## Node number 159: 8 observations
## predicted class=Y expected loss=0.375 P(node) =0.001787709
## class counts: 3 5
## probabilities: 0.375 0.625
##
## Node number 182: 197 observations, complexity param=0.0004450378
## predicted class=N expected loss=0.2944162 P(node) =0.04402235
## class counts: 139 58
## probabilities: 0.706 0.294
## left son=364 (11 obs) right son=365 (186 obs)
## Primary splits:
## WordCount.log < 6.473118 to the right, improve=0.9650178, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve=0.2082768, (0 missing)
##
## Node number 183: 7 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.001564246
## class counts: 3 4
## probabilities: 0.429 0.571
##
## Node number 188: 38 observations, complexity param=0.001335113
## predicted class=N expected loss=0.3684211 P(node) =0.00849162
## class counts: 24 14
## probabilities: 0.632 0.368
## left son=376 (16 obs) right son=377 (22 obs)
## Primary splits:
## WordCount.log < 5.908079 to the right, improve=0.7751196, (0 missing)
##
## Node number 189: 20 observations, complexity param=0.001602136
## predicted class=Y expected loss=0.45 P(node) =0.004469274
## class counts: 9 11
## probabilities: 0.450 0.550
## left son=378 (10 obs) right son=379 (10 obs)
## Primary splits:
## WordCount.log < 6.019802 to the right, improve=0.9, (0 missing)
##
## Node number 218: 70 observations
## predicted class=N expected loss=0.2 P(node) =0.01564246
## class counts: 56 14
## probabilities: 0.800 0.200
##
## Node number 219: 277 observations, complexity param=0.001335113
## predicted class=N expected loss=0.2888087 P(node) =0.06189944
## class counts: 197 80
## probabilities: 0.711 0.289
## left son=438 (193 obs) right son=439 (84 obs)
## Primary splits:
## A.nuppr.log < 1.700599 to the right, improve=0.7677911, (0 missing)
## WordCount.log < 7.175489 to the right, improve=0.4571296, (0 missing)
##
## Node number 220: 15 observations
## predicted class=N expected loss=0.2666667 P(node) =0.003351955
## class counts: 11 4
## probabilities: 0.733 0.267
##
## Node number 221: 28 observations, complexity param=0.001335113
## predicted class=N expected loss=0.4642857 P(node) =0.006256983
## class counts: 15 13
## probabilities: 0.536 0.464
## left son=442 (7 obs) right son=443 (21 obs)
## Primary splits:
## WordCount.log < 7.485772 to the right, improve=0.5952381, (0 missing)
## A.nuppr.log < 2.012676 to the left, improve=0.2387319, (0 missing)
##
## Node number 226: 112 observations, complexity param=0.0008900757
## predicted class=N expected loss=0.3214286 P(node) =0.02502793
## class counts: 76 36
## probabilities: 0.679 0.321
## left son=452 (10 obs) right son=453 (102 obs)
## Primary splits:
## WordCount.log < 6.956055 to the right, improve=1.076751, (0 missing)
##
## Node number 227: 10 observations
## predicted class=Y expected loss=0.4 P(node) =0.002234637
## class counts: 4 6
## probabilities: 0.400 0.600
##
## Node number 246: 7 observations
## predicted class=N expected loss=0.2857143 P(node) =0.001564246
## class counts: 5 2
## probabilities: 0.714 0.286
##
## Node number 247: 14 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.003128492
## class counts: 6 8
## probabilities: 0.429 0.571
##
## Node number 250: 20 observations
## predicted class=N expected loss=0.35 P(node) =0.004469274
## class counts: 13 7
## probabilities: 0.650 0.350
##
## Node number 251: 7 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.001564246
## class counts: 3 4
## probabilities: 0.429 0.571
##
## Node number 252: 9 observations
## predicted class=N expected loss=0.2222222 P(node) =0.002011173
## class counts: 7 2
## probabilities: 0.778 0.222
##
## Node number 253: 315 observations, complexity param=0.003242418
## predicted class=N expected loss=0.4857143 P(node) =0.07039106
## class counts: 162 153
## probabilities: 0.514 0.486
## left son=506 (277 obs) right son=507 (38 obs)
## Primary splits:
## WordCount.log < 7.200424 to the left, improve=1.23519400, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.07465438, (0 missing)
##
## Node number 316: 7 observations
## predicted class=N expected loss=0 P(node) =0.001564246
## class counts: 7 0
## probabilities: 1.000 0.000
##
## Node number 317: 42 observations, complexity param=0.0005340454
## predicted class=N expected loss=0.4047619 P(node) =0.009385475
## class counts: 25 17
## probabilities: 0.595 0.405
## left son=634 (35 obs) right son=635 (7 obs)
## Primary splits:
## WordCount.log < 6.110357 to the right, improve=0.4666667, (0 missing)
##
## Node number 364: 11 observations
## predicted class=N expected loss=0.09090909 P(node) =0.002458101
## class counts: 10 1
## probabilities: 0.909 0.091
##
## Node number 365: 186 observations, complexity param=0.0004450378
## predicted class=N expected loss=0.3064516 P(node) =0.04156425
## class counts: 129 57
## probabilities: 0.694 0.306
## left son=730 (161 obs) right son=731 (25 obs)
## Primary splits:
## WordCount.log < 6.418364 to the left, improve=0.5055099, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve=0.3387992, (0 missing)
##
## Node number 376: 16 observations
## predicted class=N expected loss=0.25 P(node) =0.003575419
## class counts: 12 4
## probabilities: 0.750 0.250
##
## Node number 377: 22 observations, complexity param=0.001335113
## predicted class=N expected loss=0.4545455 P(node) =0.004916201
## class counts: 12 10
## probabilities: 0.545 0.455
## left son=754 (10 obs) right son=755 (12 obs)
## Primary splits:
## WordCount.log < 5.867882 to the left, improve=0.8757576, (0 missing)
##
## Node number 378: 10 observations
## predicted class=N expected loss=0.4 P(node) =0.002234637
## class counts: 6 4
## probabilities: 0.600 0.400
##
## Node number 379: 10 observations
## predicted class=Y expected loss=0.3 P(node) =0.002234637
## class counts: 3 7
## probabilities: 0.300 0.700
##
## Node number 438: 193 observations, complexity param=0.001335113
## predicted class=N expected loss=0.2642487 P(node) =0.04312849
## class counts: 142 51
## probabilities: 0.736 0.264
## left son=876 (87 obs) right son=877 (106 obs)
## Primary splits:
## A.nuppr.log < 1.868835 to the left, improve=1.0420780, (0 missing)
## WordCount.log < 7.175489 to the right, improve=0.9782561, (0 missing)
## Surrogate splits:
## WordCount.log < 6.726233 to the left, agree=0.58, adj=0.069, (0 split)
##
## Node number 439: 84 observations, complexity param=0.001335113
## predicted class=N expected loss=0.3452381 P(node) =0.01877095
## class counts: 55 29
## probabilities: 0.655 0.345
## left son=878 (74 obs) right son=879 (10 obs)
## Primary splits:
## WordCount.log < 7.096235 to the left, improve=2.857272, (0 missing)
##
## Node number 442: 7 observations
## predicted class=N expected loss=0.2857143 P(node) =0.001564246
## class counts: 5 2
## probabilities: 0.714 0.286
##
## Node number 443: 21 observations, complexity param=0.001335113
## predicted class=Y expected loss=0.4761905 P(node) =0.004692737
## class counts: 10 11
## probabilities: 0.476 0.524
## left son=886 (13 obs) right son=887 (8 obs)
## Primary splits:
## WordCount.log < 7.410316 to the left, improve=1.3223440, (0 missing)
## A.nuppr.log < 1.868835 to the left, improve=0.5723443, (0 missing)
## Surrogate splits:
## A.nuppr.log < 2.012676 to the left, agree=0.667, adj=0.125, (0 split)
##
## Node number 452: 10 observations
## predicted class=N expected loss=0.1 P(node) =0.002234637
## class counts: 9 1
## probabilities: 0.900 0.100
##
## Node number 453: 102 observations, complexity param=0.0008900757
## predicted class=N expected loss=0.3431373 P(node) =0.0227933
## class counts: 67 35
## probabilities: 0.657 0.343
## left son=906 (73 obs) right son=907 (29 obs)
## Primary splits:
## WordCount.log < 6.837858 to the left, improve=0.8958385, (0 missing)
##
## Node number 506: 277 observations, complexity param=0.003242418
## predicted class=N expected loss=0.4693141 P(node) =0.06189944
## class counts: 147 130
## probabilities: 0.531 0.469
## left son=1012 (96 obs) right son=1013 (181 obs)
## Primary splits:
## WordCount.log < 6.934883 to the right, improve=1.586535000, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve=0.008509055, (0 missing)
##
## Node number 507: 38 observations, complexity param=0.002670227
## predicted class=Y expected loss=0.3947368 P(node) =0.00849162
## class counts: 15 23
## probabilities: 0.395 0.605
## left son=1014 (27 obs) right son=1015 (11 obs)
## Primary splits:
## WordCount.log < 7.238497 to the right, improve=0.460925, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.388664, (0 missing)
##
## Node number 634: 35 observations
## predicted class=N expected loss=0.3714286 P(node) =0.007821229
## class counts: 22 13
## probabilities: 0.629 0.371
##
## Node number 635: 7 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.001564246
## class counts: 3 4
## probabilities: 0.429 0.571
##
## Node number 730: 161 observations, complexity param=0.0004450378
## predicted class=N expected loss=0.2919255 P(node) =0.03597765
## class counts: 114 47
## probabilities: 0.708 0.292
## left son=1460 (81 obs) right son=1461 (80 obs)
## Primary splits:
## A.nuppr.log < 0.8958797 to the right, improve=0.6605494, (0 missing)
## WordCount.log < 6.394426 to the right, improve=0.4691369, (0 missing)
## Surrogate splits:
## WordCount.log < 6.369044 to the right, agree=0.565, adj=0.125, (0 split)
##
## Node number 731: 25 observations, complexity param=0.0004450378
## predicted class=N expected loss=0.4 P(node) =0.005586592
## class counts: 15 10
## probabilities: 0.600 0.400
## left son=1462 (13 obs) right son=1463 (12 obs)
## Primary splits:
## WordCount.log < 6.45126 to the right, improve=1.5512820, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.2051282, (0 missing)
##
## Node number 754: 10 observations
## predicted class=N expected loss=0.3 P(node) =0.002234637
## class counts: 7 3
## probabilities: 0.700 0.300
##
## Node number 755: 12 observations
## predicted class=Y expected loss=0.4166667 P(node) =0.002681564
## class counts: 5 7
## probabilities: 0.417 0.583
##
## Node number 876: 87 observations, complexity param=0.0006675567
## predicted class=N expected loss=0.2068966 P(node) =0.01944134
## class counts: 69 18
## probabilities: 0.793 0.207
## left son=1752 (32 obs) right son=1753 (55 obs)
## Primary splits:
## WordCount.log < 6.943571 to the right, improve=1.296042, (0 missing)
##
## Node number 877: 106 observations, complexity param=0.001335113
## predicted class=N expected loss=0.3113208 P(node) =0.02368715
## class counts: 73 33
## probabilities: 0.689 0.311
## left son=1754 (99 obs) right son=1755 (7 obs)
## Primary splits:
## WordCount.log < 6.691463 to the right, improve=1.01415800, (0 missing)
## A.nuppr.log < 2.012676 to the left, improve=0.04643749, (0 missing)
##
## Node number 878: 74 observations, complexity param=0.001335113
## predicted class=N expected loss=0.2972973 P(node) =0.01653631
## class counts: 52 22
## probabilities: 0.703 0.297
## left son=1756 (24 obs) right son=1757 (50 obs)
## Primary splits:
## WordCount.log < 6.918691 to the right, improve=1.212252, (0 missing)
##
## Node number 879: 10 observations
## predicted class=Y expected loss=0.3 P(node) =0.002234637
## class counts: 3 7
## probabilities: 0.300 0.700
##
## Node number 886: 13 observations
## predicted class=N expected loss=0.3846154 P(node) =0.002905028
## class counts: 8 5
## probabilities: 0.615 0.385
##
## Node number 887: 8 observations
## predicted class=Y expected loss=0.25 P(node) =0.001787709
## class counts: 2 6
## probabilities: 0.250 0.750
##
## Node number 906: 73 observations
## predicted class=N expected loss=0.3013699 P(node) =0.01631285
## class counts: 51 22
## probabilities: 0.699 0.301
##
## Node number 907: 29 observations, complexity param=0.0008900757
## predicted class=N expected loss=0.4482759 P(node) =0.006480447
## class counts: 16 13
## probabilities: 0.552 0.448
## left son=1814 (19 obs) right son=1815 (10 obs)
## Primary splits:
## WordCount.log < 6.893146 to the right, improve=0.7027223, (0 missing)
##
## Node number 1012: 96 observations, complexity param=0.00200267
## predicted class=N expected loss=0.3958333 P(node) =0.02145251
## class counts: 58 38
## probabilities: 0.604 0.396
## left son=2024 (7 obs) right son=2025 (89 obs)
## Primary splits:
## WordCount.log < 6.947457 to the left, improve=2.3661050, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve=0.8878418, (0 missing)
##
## Node number 1013: 181 observations, complexity param=0.003242418
## predicted class=Y expected loss=0.4917127 P(node) =0.04044693
## class counts: 89 92
## probabilities: 0.492 0.508
## left son=2026 (173 obs) right son=2027 (8 obs)
## Primary splits:
## WordCount.log < 6.91721 to the left, improve=0.9780283, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.2280707, (0 missing)
##
## Node number 1014: 27 observations, complexity param=0.002670227
## predicted class=Y expected loss=0.4444444 P(node) =0.00603352
## class counts: 12 15
## probabilities: 0.444 0.556
## left son=2028 (8 obs) right son=2029 (19 obs)
## Primary splits:
## A.nuppr.log < 0.8958797 to the left, improve=2.1228070, (0 missing)
## WordCount.log < 7.258411 to the left, improve=0.7412281, (0 missing)
## Surrogate splits:
## WordCount.log < 7.260522 to the left, agree=0.741, adj=0.125, (0 split)
##
## Node number 1015: 11 observations
## predicted class=Y expected loss=0.2727273 P(node) =0.002458101
## class counts: 3 8
## probabilities: 0.273 0.727
##
## Node number 1460: 81 observations, complexity param=0.0004450378
## predicted class=N expected loss=0.2469136 P(node) =0.01810056
## class counts: 61 20
## probabilities: 0.753 0.247
## left son=2920 (47 obs) right son=2921 (34 obs)
## Primary splits:
## WordCount.log < 6.276642 to the left, improve=0.6879124, (0 missing)
##
## Node number 1461: 80 observations, complexity param=0.0004450378
## predicted class=N expected loss=0.3375 P(node) =0.01787709
## class counts: 53 27
## probabilities: 0.662 0.337
## left son=2922 (22 obs) right son=2923 (58 obs)
## Primary splits:
## WordCount.log < 6.312638 to the right, improve=1.470925, (0 missing)
##
## Node number 1462: 13 observations
## predicted class=N expected loss=0.2307692 P(node) =0.002905028
## class counts: 10 3
## probabilities: 0.769 0.231
##
## Node number 1463: 12 observations
## predicted class=Y expected loss=0.4166667 P(node) =0.002681564
## class counts: 5 7
## probabilities: 0.417 0.583
##
## Node number 1752: 32 observations
## predicted class=N expected loss=0.09375 P(node) =0.007150838
## class counts: 29 3
## probabilities: 0.906 0.094
##
## Node number 1753: 55 observations, complexity param=0.0006675567
## predicted class=N expected loss=0.2727273 P(node) =0.0122905
## class counts: 40 15
## probabilities: 0.727 0.273
## left son=3506 (48 obs) right son=3507 (7 obs)
## Primary splits:
## WordCount.log < 6.897704 to the left, improve=1.431277, (0 missing)
##
## Node number 1754: 99 observations, complexity param=0.001335113
## predicted class=N expected loss=0.2929293 P(node) =0.02212291
## class counts: 70 29
## probabilities: 0.707 0.293
## left son=3508 (41 obs) right son=3509 (58 obs)
## Primary splits:
## WordCount.log < 6.905207 to the left, improve=0.7544240, (0 missing)
## A.nuppr.log < 2.012676 to the left, improve=0.1405358, (0 missing)
##
## Node number 1755: 7 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.001564246
## class counts: 3 4
## probabilities: 0.429 0.571
##
## Node number 1756: 24 observations
## predicted class=N expected loss=0.1666667 P(node) =0.005363128
## class counts: 20 4
## probabilities: 0.833 0.167
##
## Node number 1757: 50 observations, complexity param=0.001335113
## predicted class=N expected loss=0.36 P(node) =0.01117318
## class counts: 32 18
## probabilities: 0.640 0.360
## left son=3514 (41 obs) right son=3515 (9 obs)
## Primary splits:
## WordCount.log < 6.850652 to the left, improve=2.06439, (0 missing)
##
## Node number 1814: 19 observations
## predicted class=N expected loss=0.3684211 P(node) =0.00424581
## class counts: 12 7
## probabilities: 0.632 0.368
##
## Node number 1815: 10 observations
## predicted class=Y expected loss=0.4 P(node) =0.002234637
## class counts: 4 6
## probabilities: 0.400 0.600
##
## Node number 2024: 7 observations
## predicted class=N expected loss=0 P(node) =0.001564246
## class counts: 7 0
## probabilities: 1.000 0.000
##
## Node number 2025: 89 observations, complexity param=0.00200267
## predicted class=N expected loss=0.4269663 P(node) =0.01988827
## class counts: 51 38
## probabilities: 0.573 0.427
## left son=4050 (82 obs) right son=4051 (7 obs)
## Primary splits:
## WordCount.log < 6.96319 to the right, improve=1.2543950, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve=0.7241585, (0 missing)
##
## Node number 2026: 173 observations, complexity param=0.003242418
## predicted class=N expected loss=0.4971098 P(node) =0.03865922
## class counts: 87 86
## probabilities: 0.503 0.497
## left son=4052 (166 obs) right son=4053 (7 obs)
## Primary splits:
## WordCount.log < 6.668225 to the right, improve=0.6881597, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.0250218, (0 missing)
##
## Node number 2027: 8 observations
## predicted class=Y expected loss=0.25 P(node) =0.001787709
## class counts: 2 6
## probabilities: 0.250 0.750
##
## Node number 2028: 8 observations
## predicted class=N expected loss=0.25 P(node) =0.001787709
## class counts: 6 2
## probabilities: 0.750 0.250
##
## Node number 2029: 19 observations
## predicted class=Y expected loss=0.3157895 P(node) =0.00424581
## class counts: 6 13
## probabilities: 0.316 0.684
##
## Node number 2920: 47 observations, complexity param=0.0004450378
## predicted class=N expected loss=0.1914894 P(node) =0.01050279
## class counts: 38 9
## probabilities: 0.809 0.191
## left son=5840 (23 obs) right son=5841 (24 obs)
## Primary splits:
## WordCount.log < 6.19338 to the right, improve=0.9843509, (0 missing)
##
## Node number 2921: 34 observations
## predicted class=N expected loss=0.3235294 P(node) =0.007597765
## class counts: 23 11
## probabilities: 0.676 0.324
##
## Node number 2922: 22 observations
## predicted class=N expected loss=0.1818182 P(node) =0.004916201
## class counts: 18 4
## probabilities: 0.818 0.182
##
## Node number 2923: 58 observations, complexity param=0.0004450378
## predicted class=N expected loss=0.3965517 P(node) =0.01296089
## class counts: 35 23
## probabilities: 0.603 0.397
## left son=5846 (30 obs) right son=5847 (28 obs)
## Primary splits:
## WordCount.log < 6.240268 to the left, improve=1.158621, (0 missing)
##
## Node number 3506: 48 observations
## predicted class=N expected loss=0.2291667 P(node) =0.01072626
## class counts: 37 11
## probabilities: 0.771 0.229
##
## Node number 3507: 7 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.001564246
## class counts: 3 4
## probabilities: 0.429 0.571
##
## Node number 3508: 41 observations
## predicted class=N expected loss=0.2195122 P(node) =0.009162011
## class counts: 32 9
## probabilities: 0.780 0.220
##
## Node number 3509: 58 observations, complexity param=0.001335113
## predicted class=N expected loss=0.3448276 P(node) =0.01296089
## class counts: 38 20
## probabilities: 0.655 0.345
## left son=7018 (51 obs) right son=7019 (7 obs)
## Primary splits:
## WordCount.log < 6.937798 to the right, improve=2.1732830, (0 missing)
## A.nuppr.log < 2.012676 to the right, improve=0.2300626, (0 missing)
##
## Node number 3514: 41 observations
## predicted class=N expected loss=0.2926829 P(node) =0.009162011
## class counts: 29 12
## probabilities: 0.707 0.293
##
## Node number 3515: 9 observations
## predicted class=Y expected loss=0.3333333 P(node) =0.002011173
## class counts: 3 6
## probabilities: 0.333 0.667
##
## Node number 4050: 82 observations, complexity param=0.00200267
## predicted class=N expected loss=0.402439 P(node) =0.01832402
## class counts: 49 33
## probabilities: 0.598 0.402
## left son=8100 (13 obs) right son=8101 (69 obs)
## Primary splits:
## WordCount.log < 6.999876 to the left, improve=0.9105963, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve=0.4335533, (0 missing)
##
## Node number 4051: 7 observations
## predicted class=Y expected loss=0.2857143 P(node) =0.001564246
## class counts: 2 5
## probabilities: 0.286 0.714
##
## Node number 4052: 166 observations, complexity param=0.003242418
## predicted class=N expected loss=0.4879518 P(node) =0.03709497
## class counts: 85 81
## probabilities: 0.512 0.488
## left son=8104 (48 obs) right son=8105 (118 obs)
## Primary splits:
## WordCount.log < 6.736373 to the left, improve=1.1460160, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.1819937, (0 missing)
##
## Node number 4053: 7 observations
## predicted class=Y expected loss=0.2857143 P(node) =0.001564246
## class counts: 2 5
## probabilities: 0.286 0.714
##
## Node number 5840: 23 observations
## predicted class=N expected loss=0.08695652 P(node) =0.005139665
## class counts: 21 2
## probabilities: 0.913 0.087
##
## Node number 5841: 24 observations, complexity param=0.0004450378
## predicted class=N expected loss=0.2916667 P(node) =0.005363128
## class counts: 17 7
## probabilities: 0.708 0.292
## left son=11682 (17 obs) right son=11683 (7 obs)
## Primary splits:
## WordCount.log < 6.160151 to the left, improve=1.546919, (0 missing)
##
## Node number 5846: 30 observations
## predicted class=N expected loss=0.3 P(node) =0.006703911
## class counts: 21 9
## probabilities: 0.700 0.300
##
## Node number 5847: 28 observations, complexity param=0.0004450378
## predicted class=N expected loss=0.5 P(node) =0.006256983
## class counts: 14 14
## probabilities: 0.500 0.500
## left son=11694 (20 obs) right son=11695 (8 obs)
## Primary splits:
## WordCount.log < 6.254789 to the right, improve=0.35, (0 missing)
##
## Node number 7018: 51 observations
## predicted class=N expected loss=0.2941176 P(node) =0.01139665
## class counts: 36 15
## probabilities: 0.706 0.294
##
## Node number 7019: 7 observations
## predicted class=Y expected loss=0.2857143 P(node) =0.001564246
## class counts: 2 5
## probabilities: 0.286 0.714
##
## Node number 8100: 13 observations
## predicted class=N expected loss=0.2307692 P(node) =0.002905028
## class counts: 10 3
## probabilities: 0.769 0.231
##
## Node number 8101: 69 observations, complexity param=0.00200267
## predicted class=N expected loss=0.4347826 P(node) =0.01541899
## class counts: 39 30
## probabilities: 0.565 0.435
## left son=16202 (62 obs) right son=16203 (7 obs)
## Primary splits:
## WordCount.log < 7.027311 to the right, improve=1.2171910, (0 missing)
## A.nuppr.log < 0.8958797 to the right, improve=0.8095952, (0 missing)
##
## Node number 8104: 48 observations, complexity param=0.00200267
## predicted class=N expected loss=0.3958333 P(node) =0.01072626
## class counts: 29 19
## probabilities: 0.604 0.396
## left son=16208 (25 obs) right son=16209 (23 obs)
## Primary splits:
## A.nuppr.log < 0.8958797 to the left, improve=0.6000725, (0 missing)
## WordCount.log < 6.718409 to the right, improve=0.3333333, (0 missing)
## Surrogate splits:
## WordCount.log < 6.727431 to the left, agree=0.625, adj=0.217, (0 split)
##
## Node number 8105: 118 observations, complexity param=0.003242418
## predicted class=Y expected loss=0.4745763 P(node) =0.02636872
## class counts: 56 62
## probabilities: 0.475 0.525
## left son=16210 (90 obs) right son=16211 (28 obs)
## Primary splits:
## WordCount.log < 6.777644 to the right, improve=3.7030130000, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.0007043804, (0 missing)
##
## Node number 11682: 17 observations
## predicted class=N expected loss=0.1764706 P(node) =0.003798883
## class counts: 14 3
## probabilities: 0.824 0.176
##
## Node number 11683: 7 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.001564246
## class counts: 3 4
## probabilities: 0.429 0.571
##
## Node number 11694: 20 observations, complexity param=0.0004450378
## predicted class=N expected loss=0.45 P(node) =0.004469274
## class counts: 11 9
## probabilities: 0.550 0.450
## left son=23388 (7 obs) right son=23389 (13 obs)
## Primary splits:
## WordCount.log < 6.27382 to the left, improve=0.5813187, (0 missing)
##
## Node number 11695: 8 observations
## predicted class=Y expected loss=0.375 P(node) =0.001787709
## class counts: 3 5
## probabilities: 0.375 0.625
##
## Node number 16202: 62 observations, complexity param=0.00200267
## predicted class=N expected loss=0.4032258 P(node) =0.01385475
## class counts: 37 25
## probabilities: 0.597 0.403
## left son=32404 (24 obs) right son=32405 (38 obs)
## Primary splits:
## A.nuppr.log < 0.8958797 to the right, improve=0.9746746, (0 missing)
## WordCount.log < 7.100027 to the right, improve=0.2903226, (0 missing)
## Surrogate splits:
## WordCount.log < 7.191429 to the right, agree=0.694, adj=0.208, (0 split)
##
## Node number 16203: 7 observations
## predicted class=Y expected loss=0.2857143 P(node) =0.001564246
## class counts: 2 5
## probabilities: 0.286 0.714
##
## Node number 16208: 25 observations
## predicted class=N expected loss=0.32 P(node) =0.005586592
## class counts: 17 8
## probabilities: 0.680 0.320
##
## Node number 16209: 23 observations, complexity param=0.00200267
## predicted class=N expected loss=0.4782609 P(node) =0.005139665
## class counts: 12 11
## probabilities: 0.522 0.478
## left son=32418 (8 obs) right son=32419 (15 obs)
## Primary splits:
## WordCount.log < 6.72323 to the right, improve=1.278261, (0 missing)
##
## Node number 16210: 90 observations, complexity param=0.00200267
## predicted class=N expected loss=0.4555556 P(node) =0.02011173
## class counts: 49 41
## probabilities: 0.544 0.456
## left son=32420 (7 obs) right son=32421 (83 obs)
## Primary splits:
## WordCount.log < 6.786152 to the left, improve=1.48437600, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.06706349, (0 missing)
##
## Node number 16211: 28 observations
## predicted class=Y expected loss=0.25 P(node) =0.006256983
## class counts: 7 21
## probabilities: 0.250 0.750
##
## Node number 23388: 7 observations
## predicted class=N expected loss=0.2857143 P(node) =0.001564246
## class counts: 5 2
## probabilities: 0.714 0.286
##
## Node number 23389: 13 observations
## predicted class=Y expected loss=0.4615385 P(node) =0.002905028
## class counts: 6 7
## probabilities: 0.462 0.538
##
## Node number 32404: 24 observations
## predicted class=N expected loss=0.2916667 P(node) =0.005363128
## class counts: 17 7
## probabilities: 0.708 0.292
##
## Node number 32405: 38 observations, complexity param=0.00200267
## predicted class=N expected loss=0.4736842 P(node) =0.00849162
## class counts: 20 18
## probabilities: 0.526 0.474
## left son=64810 (15 obs) right son=64811 (23 obs)
## Primary splits:
## WordCount.log < 7.069874 to the left, improve=0.9763539, (0 missing)
##
## Node number 32418: 8 observations
## predicted class=N expected loss=0.25 P(node) =0.001787709
## class counts: 6 2
## probabilities: 0.750 0.250
##
## Node number 32419: 15 observations
## predicted class=Y expected loss=0.4 P(node) =0.003351955
## class counts: 6 9
## probabilities: 0.400 0.600
##
## Node number 32420: 7 observations
## predicted class=N expected loss=0.1428571 P(node) =0.001564246
## class counts: 6 1
## probabilities: 0.857 0.143
##
## Node number 32421: 83 observations, complexity param=0.00200267
## predicted class=N expected loss=0.4819277 P(node) =0.01854749
## class counts: 43 40
## probabilities: 0.518 0.482
## left son=64842 (76 obs) right son=64843 (7 obs)
## Primary splits:
## WordCount.log < 6.791783 to the right, improve=0.8254824, (0 missing)
## A.nuppr.log < 0.8958797 to the left, improve=0.2761925, (0 missing)
##
## Node number 64810: 15 observations
## predicted class=N expected loss=0.3333333 P(node) =0.003351955
## class counts: 10 5
## probabilities: 0.667 0.333
##
## Node number 64811: 23 observations, complexity param=0.00200267
## predicted class=Y expected loss=0.4347826 P(node) =0.005139665
## class counts: 10 13
## probabilities: 0.435 0.565
## left son=129622 (14 obs) right son=129623 (9 obs)
## Primary splits:
## WordCount.log < 7.115987 to the right, improve=1.336094, (0 missing)
##
## Node number 64842: 76 observations, complexity param=0.00200267
## predicted class=N expected loss=0.4605263 P(node) =0.01698324
## class counts: 41 35
## probabilities: 0.539 0.461
## left son=129684 (42 obs) right son=129685 (34 obs)
## Primary splits:
## A.nuppr.log < 0.8958797 to the left, improve=0.5838862, (0 missing)
## WordCount.log < 6.821652 to the left, improve=0.5069266, (0 missing)
## Surrogate splits:
## WordCount.log < 6.88295 to the left, agree=0.618, adj=0.147, (0 split)
##
## Node number 64843: 7 observations
## predicted class=Y expected loss=0.2857143 P(node) =0.001564246
## class counts: 2 5
## probabilities: 0.286 0.714
##
## Node number 129622: 14 observations
## predicted class=N expected loss=0.4285714 P(node) =0.003128492
## class counts: 8 6
## probabilities: 0.571 0.429
##
## Node number 129623: 9 observations
## predicted class=Y expected loss=0.2222222 P(node) =0.002011173
## class counts: 2 7
## probabilities: 0.222 0.778
##
## Node number 129684: 42 observations, complexity param=0.001335113
## predicted class=N expected loss=0.4047619 P(node) =0.009385475
## class counts: 25 17
## probabilities: 0.595 0.405
## left son=259368 (10 obs) right son=259369 (32 obs)
## Primary splits:
## WordCount.log < 6.821652 to the left, improve=0.2880952, (0 missing)
##
## Node number 129685: 34 observations, complexity param=0.001335113
## predicted class=Y expected loss=0.4705882 P(node) =0.007597765
## class counts: 16 18
## probabilities: 0.471 0.529
## left son=259370 (19 obs) right son=259371 (15 obs)
## Primary splits:
## WordCount.log < 6.850126 to the right, improve=0.2674923, (0 missing)
##
## Node number 259368: 10 observations
## predicted class=N expected loss=0.3 P(node) =0.002234637
## class counts: 7 3
## probabilities: 0.700 0.300
##
## Node number 259369: 32 observations, complexity param=0.001335113
## predicted class=N expected loss=0.4375 P(node) =0.007150838
## class counts: 18 14
## probabilities: 0.562 0.437
## left son=518738 (22 obs) right son=518739 (10 obs)
## Primary splits:
## WordCount.log < 6.841615 to the right, improve=0.7681818, (0 missing)
##
## Node number 259370: 19 observations
## predicted class=N expected loss=0.4736842 P(node) =0.00424581
## class counts: 10 9
## probabilities: 0.526 0.474
##
## Node number 259371: 15 observations
## predicted class=Y expected loss=0.4 P(node) =0.003351955
## class counts: 6 9
## probabilities: 0.400 0.600
##
## Node number 518738: 22 observations
## predicted class=N expected loss=0.3636364 P(node) =0.004916201
## class counts: 14 8
## probabilities: 0.636 0.364
##
## Node number 518739: 10 observations
## predicted class=Y expected loss=0.4 P(node) =0.002234637
## class counts: 4 6
## probabilities: 0.400 0.600
##
## n= 4475
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4475 749 N (0.83262570 0.16737430)
## 2) WordCount.log< 6.524296 3266 330 N (0.89895897 0.10104103)
## 4) A.nuppr.log>=1.242453 2468 139 N (0.94367909 0.05632091)
## 8) WordCount.log< 6.019808 1836 55 N (0.97004357 0.02995643) *
## 9) WordCount.log>=6.019808 632 84 N (0.86708861 0.13291139)
## 18) A.nuppr.log>=1.497866 486 50 N (0.89711934 0.10288066) *
## 19) A.nuppr.log< 1.497866 146 34 N (0.76712329 0.23287671)
## 38) WordCount.log>=6.281328 67 10 N (0.85074627 0.14925373) *
## 39) WordCount.log< 6.281328 79 24 N (0.69620253 0.30379747)
## 78) WordCount.log< 6.094697 22 2 N (0.90909091 0.09090909) *
## 79) WordCount.log>=6.094697 57 22 N (0.61403509 0.38596491)
## 158) WordCount.log< 6.244165 49 17 N (0.65306122 0.34693878)
## 316) WordCount.log>=6.233429 7 0 N (1.00000000 0.00000000) *
## 317) WordCount.log< 6.233429 42 17 N (0.59523810 0.40476190)
## 634) WordCount.log>=6.110357 35 13 N (0.62857143 0.37142857) *
## 635) WordCount.log< 6.110357 7 3 Y (0.42857143 0.57142857) *
## 159) WordCount.log>=6.244165 8 3 Y (0.37500000 0.62500000) *
## 5) A.nuppr.log< 1.242453 798 191 N (0.76065163 0.23934837)
## 10) WordCount.log< 5.736551 429 71 N (0.83449883 0.16550117) *
## 11) WordCount.log>=5.736551 369 120 N (0.67479675 0.32520325)
## 22) WordCount.log>=6.085637 223 63 N (0.71748879 0.28251121)
## 44) WordCount.log< 6.102556 8 0 N (1.00000000 0.00000000) *
## 45) WordCount.log>=6.102556 215 63 N (0.70697674 0.29302326)
## 90) WordCount.log>=6.505783 11 1 N (0.90909091 0.09090909) *
## 91) WordCount.log< 6.505783 204 62 N (0.69607843 0.30392157)
## 182) WordCount.log< 6.489964 197 58 N (0.70558376 0.29441624)
## 364) WordCount.log>=6.473118 11 1 N (0.90909091 0.09090909) *
## 365) WordCount.log< 6.473118 186 57 N (0.69354839 0.30645161)
## 730) WordCount.log< 6.418364 161 47 N (0.70807453 0.29192547)
## 1460) A.nuppr.log>=0.8958797 81 20 N (0.75308642 0.24691358)
## 2920) WordCount.log< 6.276642 47 9 N (0.80851064 0.19148936)
## 5840) WordCount.log>=6.19338 23 2 N (0.91304348 0.08695652) *
## 5841) WordCount.log< 6.19338 24 7 N (0.70833333 0.29166667)
## 11682) WordCount.log< 6.160151 17 3 N (0.82352941 0.17647059) *
## 11683) WordCount.log>=6.160151 7 3 Y (0.42857143 0.57142857) *
## 2921) WordCount.log>=6.276642 34 11 N (0.67647059 0.32352941) *
## 1461) A.nuppr.log< 0.8958797 80 27 N (0.66250000 0.33750000)
## 2922) WordCount.log>=6.312638 22 4 N (0.81818182 0.18181818) *
## 2923) WordCount.log< 6.312638 58 23 N (0.60344828 0.39655172)
## 5846) WordCount.log< 6.240268 30 9 N (0.70000000 0.30000000) *
## 5847) WordCount.log>=6.240268 28 14 N (0.50000000 0.50000000)
## 11694) WordCount.log>=6.254789 20 9 N (0.55000000 0.45000000)
## 23388) WordCount.log< 6.27382 7 2 N (0.71428571 0.28571429) *
## 23389) WordCount.log>=6.27382 13 6 Y (0.46153846 0.53846154) *
## 11695) WordCount.log< 6.254789 8 3 Y (0.37500000 0.62500000) *
## 731) WordCount.log>=6.418364 25 10 N (0.60000000 0.40000000)
## 1462) WordCount.log>=6.45126 13 3 N (0.76923077 0.23076923) *
## 1463) WordCount.log< 6.45126 12 5 Y (0.41666667 0.58333333) *
## 183) WordCount.log>=6.489964 7 3 Y (0.42857143 0.57142857) *
## 23) WordCount.log< 6.085637 146 57 N (0.60958904 0.39041096)
## 46) A.nuppr.log>=0.8958797 70 20 N (0.71428571 0.28571429) *
## 47) A.nuppr.log< 0.8958797 76 37 N (0.51315789 0.48684211)
## 94) WordCount.log>=5.821556 58 25 N (0.56896552 0.43103448)
## 188) WordCount.log< 5.971259 38 14 N (0.63157895 0.36842105)
## 376) WordCount.log>=5.908079 16 4 N (0.75000000 0.25000000) *
## 377) WordCount.log< 5.908079 22 10 N (0.54545455 0.45454545)
## 754) WordCount.log< 5.867882 10 3 N (0.70000000 0.30000000) *
## 755) WordCount.log>=5.867882 12 5 Y (0.41666667 0.58333333) *
## 189) WordCount.log>=5.971259 20 9 Y (0.45000000 0.55000000)
## 378) WordCount.log>=6.019802 10 4 N (0.60000000 0.40000000) *
## 379) WordCount.log< 6.019802 10 3 Y (0.30000000 0.70000000) *
## 95) WordCount.log< 5.821556 18 6 Y (0.33333333 0.66666667) *
## 3) WordCount.log>=6.524296 1209 419 N (0.65343259 0.34656741)
## 6) A.nuppr.log>=1.497866 560 137 N (0.75535714 0.24464286)
## 12) A.nuppr.log>=2.249905 93 10 N (0.89247312 0.10752688) *
## 13) A.nuppr.log< 2.249905 467 127 N (0.72805139 0.27194861)
## 26) WordCount.log>=7.602894 33 5 N (0.84848485 0.15151515) *
## 27) WordCount.log< 7.602894 434 122 N (0.71889401 0.28110599)
## 54) WordCount.log< 7.257002 383 99 N (0.74151436 0.25848564)
## 108) A.nuppr.log>=2.138333 36 5 N (0.86111111 0.13888889) *
## 109) A.nuppr.log< 2.138333 347 94 N (0.72910663 0.27089337)
## 218) WordCount.log< 6.656083 70 14 N (0.80000000 0.20000000) *
## 219) WordCount.log>=6.656083 277 80 N (0.71119134 0.28880866)
## 438) A.nuppr.log>=1.700599 193 51 N (0.73575130 0.26424870)
## 876) A.nuppr.log< 1.868835 87 18 N (0.79310345 0.20689655)
## 1752) WordCount.log>=6.943571 32 3 N (0.90625000 0.09375000) *
## 1753) WordCount.log< 6.943571 55 15 N (0.72727273 0.27272727)
## 3506) WordCount.log< 6.897704 48 11 N (0.77083333 0.22916667) *
## 3507) WordCount.log>=6.897704 7 3 Y (0.42857143 0.57142857) *
## 877) A.nuppr.log>=1.868835 106 33 N (0.68867925 0.31132075)
## 1754) WordCount.log>=6.691463 99 29 N (0.70707071 0.29292929)
## 3508) WordCount.log< 6.905207 41 9 N (0.78048780 0.21951220) *
## 3509) WordCount.log>=6.905207 58 20 N (0.65517241 0.34482759)
## 7018) WordCount.log>=6.937798 51 15 N (0.70588235 0.29411765) *
## 7019) WordCount.log< 6.937798 7 2 Y (0.28571429 0.71428571) *
## 1755) WordCount.log< 6.691463 7 3 Y (0.42857143 0.57142857) *
## 439) A.nuppr.log< 1.700599 84 29 N (0.65476190 0.34523810)
## 878) WordCount.log< 7.096235 74 22 N (0.70270270 0.29729730)
## 1756) WordCount.log>=6.918691 24 4 N (0.83333333 0.16666667) *
## 1757) WordCount.log< 6.918691 50 18 N (0.64000000 0.36000000)
## 3514) WordCount.log< 6.850652 41 12 N (0.70731707 0.29268293) *
## 3515) WordCount.log>=6.850652 9 3 Y (0.33333333 0.66666667) *
## 879) WordCount.log>=7.096235 10 3 Y (0.30000000 0.70000000) *
## 55) WordCount.log>=7.257002 51 23 N (0.54901961 0.45098039)
## 110) WordCount.log>=7.282065 43 17 N (0.60465116 0.39534884)
## 220) A.nuppr.log< 1.700599 15 4 N (0.73333333 0.26666667) *
## 221) A.nuppr.log>=1.700599 28 13 N (0.53571429 0.46428571)
## 442) WordCount.log>=7.485772 7 2 N (0.71428571 0.28571429) *
## 443) WordCount.log< 7.485772 21 10 Y (0.47619048 0.52380952)
## 886) WordCount.log< 7.410316 13 5 N (0.61538462 0.38461538) *
## 887) WordCount.log>=7.410316 8 2 Y (0.25000000 0.75000000) *
## 111) WordCount.log< 7.282065 8 2 Y (0.25000000 0.75000000) *
## 7) A.nuppr.log< 1.497866 649 282 N (0.56548536 0.43451464)
## 14) A.nuppr.log>=1.242453 194 74 N (0.61855670 0.38144330)
## 28) WordCount.log>=6.588926 181 65 N (0.64088398 0.35911602)
## 56) WordCount.log< 7.087571 132 43 N (0.67424242 0.32575758)
## 112) WordCount.log>=7.044469 10 1 N (0.90000000 0.10000000) *
## 113) WordCount.log< 7.044469 122 42 N (0.65573770 0.34426230)
## 226) WordCount.log< 7.003059 112 36 N (0.67857143 0.32142857)
## 452) WordCount.log>=6.956055 10 1 N (0.90000000 0.10000000) *
## 453) WordCount.log< 6.956055 102 35 N (0.65686275 0.34313725)
## 906) WordCount.log< 6.837858 73 22 N (0.69863014 0.30136986) *
## 907) WordCount.log>=6.837858 29 13 N (0.55172414 0.44827586)
## 1814) WordCount.log>=6.893146 19 7 N (0.63157895 0.36842105) *
## 1815) WordCount.log< 6.893146 10 4 Y (0.40000000 0.60000000) *
## 227) WordCount.log>=7.003059 10 4 Y (0.40000000 0.60000000) *
## 57) WordCount.log>=7.087571 49 22 N (0.55102041 0.44897959)
## 114) WordCount.log>=7.170483 35 13 N (0.62857143 0.37142857) *
## 115) WordCount.log< 7.170483 14 5 Y (0.35714286 0.64285714) *
## 29) WordCount.log< 6.588926 13 4 Y (0.30769231 0.69230769) *
## 15) A.nuppr.log< 1.242453 455 208 N (0.54285714 0.45714286)
## 30) WordCount.log< 6.656083 72 23 N (0.68055556 0.31944444)
## 60) A.nuppr.log< 0.8958797 37 9 N (0.75675676 0.24324324) *
## 61) A.nuppr.log>=0.8958797 35 14 N (0.60000000 0.40000000)
## 122) WordCount.log< 6.602587 14 4 N (0.71428571 0.28571429) *
## 123) WordCount.log>=6.602587 21 10 N (0.52380952 0.47619048)
## 246) WordCount.log>=6.642486 7 2 N (0.71428571 0.28571429) *
## 247) WordCount.log< 6.642486 14 6 Y (0.42857143 0.57142857) *
## 31) WordCount.log>=6.656083 383 185 N (0.51697128 0.48302872)
## 62) WordCount.log>=7.573528 35 12 N (0.65714286 0.34285714)
## 124) WordCount.log< 7.647309 8 1 N (0.87500000 0.12500000) *
## 125) WordCount.log>=7.647309 27 11 N (0.59259259 0.40740741)
## 250) A.nuppr.log< 0.8958797 20 7 N (0.65000000 0.35000000) *
## 251) A.nuppr.log>=0.8958797 7 3 Y (0.42857143 0.57142857) *
## 63) WordCount.log< 7.573528 348 173 N (0.50287356 0.49712644)
## 126) WordCount.log< 7.416671 324 155 N (0.52160494 0.47839506)
## 252) WordCount.log>=7.374931 9 2 N (0.77777778 0.22222222) *
## 253) WordCount.log< 7.374931 315 153 N (0.51428571 0.48571429)
## 506) WordCount.log< 7.200424 277 130 N (0.53068592 0.46931408)
## 1012) WordCount.log>=6.934883 96 38 N (0.60416667 0.39583333)
## 2024) WordCount.log< 6.947457 7 0 N (1.00000000 0.00000000) *
## 2025) WordCount.log>=6.947457 89 38 N (0.57303371 0.42696629)
## 4050) WordCount.log>=6.96319 82 33 N (0.59756098 0.40243902)
## 8100) WordCount.log< 6.999876 13 3 N (0.76923077 0.23076923) *
## 8101) WordCount.log>=6.999876 69 30 N (0.56521739 0.43478261)
## 16202) WordCount.log>=7.027311 62 25 N (0.59677419 0.40322581)
## 32404) A.nuppr.log>=0.8958797 24 7 N (0.70833333 0.29166667) *
## 32405) A.nuppr.log< 0.8958797 38 18 N (0.52631579 0.47368421)
## 64810) WordCount.log< 7.069874 15 5 N (0.66666667 0.33333333) *
## 64811) WordCount.log>=7.069874 23 10 Y (0.43478261 0.56521739)
## 129622) WordCount.log>=7.115987 14 6 N (0.57142857 0.42857143) *
## 129623) WordCount.log< 7.115987 9 2 Y (0.22222222 0.77777778) *
## 16203) WordCount.log< 7.027311 7 2 Y (0.28571429 0.71428571) *
## 4051) WordCount.log< 6.96319 7 2 Y (0.28571429 0.71428571) *
## 1013) WordCount.log< 6.934883 181 89 Y (0.49171271 0.50828729)
## 2026) WordCount.log< 6.91721 173 86 N (0.50289017 0.49710983)
## 4052) WordCount.log>=6.668225 166 81 N (0.51204819 0.48795181)
## 8104) WordCount.log< 6.736373 48 19 N (0.60416667 0.39583333)
## 16208) A.nuppr.log< 0.8958797 25 8 N (0.68000000 0.32000000) *
## 16209) A.nuppr.log>=0.8958797 23 11 N (0.52173913 0.47826087)
## 32418) WordCount.log>=6.72323 8 2 N (0.75000000 0.25000000) *
## 32419) WordCount.log< 6.72323 15 6 Y (0.40000000 0.60000000) *
## 8105) WordCount.log>=6.736373 118 56 Y (0.47457627 0.52542373)
## 16210) WordCount.log>=6.777644 90 41 N (0.54444444 0.45555556)
## 32420) WordCount.log< 6.786152 7 1 N (0.85714286 0.14285714) *
## 32421) WordCount.log>=6.786152 83 40 N (0.51807229 0.48192771)
## 64842) WordCount.log>=6.791783 76 35 N (0.53947368 0.46052632)
## 129684) A.nuppr.log< 0.8958797 42 17 N (0.59523810 0.40476190)
## 259368) WordCount.log< 6.821652 10 3 N (0.70000000 0.30000000) *
## 259369) WordCount.log>=6.821652 32 14 N (0.56250000 0.43750000)
## 518738) WordCount.log>=6.841615 22 8 N (0.63636364 0.36363636) *
## 518739) WordCount.log< 6.841615 10 4 Y (0.40000000 0.60000000) *
## 129685) A.nuppr.log>=0.8958797 34 16 Y (0.47058824 0.52941176)
## 259370) WordCount.log>=6.850126 19 9 N (0.52631579 0.47368421) *
## 259371) WordCount.log< 6.850126 15 6 Y (0.40000000 0.60000000) *
## 64843) WordCount.log< 6.791783 7 2 Y (0.28571429 0.71428571) *
## 16211) WordCount.log< 6.777644 28 7 Y (0.25000000 0.75000000) *
## 4053) WordCount.log< 6.668225 7 2 Y (0.28571429 0.71428571) *
## 2027) WordCount.log>=6.91721 8 2 Y (0.25000000 0.75000000) *
## 507) WordCount.log>=7.200424 38 15 Y (0.39473684 0.60526316)
## 1014) WordCount.log>=7.238497 27 12 Y (0.44444444 0.55555556)
## 2028) A.nuppr.log< 0.8958797 8 2 N (0.75000000 0.25000000) *
## 2029) A.nuppr.log>=0.8958797 19 6 Y (0.31578947 0.68421053) *
## 1015) WordCount.log< 7.238497 11 3 Y (0.27272727 0.72727273) *
## 127) WordCount.log>=7.416671 24 6 Y (0.25000000 0.75000000) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.4206960
## 3 0.2 0.5125926
## 4 0.3 0.5137615
## 5 0.4 0.4663830
## 6 0.5 0.4535902
## 7 0.6 0.3689892
## 8 0.7 0.2466667
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 3341
## 2 Y 357
## Popular.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 385
## 2 392
## Prediction
## Reference N Y
## N 3341 385
## Y 357 392
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8341899 0.4138559 0.8229644 0.8449775 0.8326257
## AccuracyPValue McnemarPValue
## 0.3989232 0.3215874
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.3994528
## 3 0.2 0.4240506
## 4 0.3 0.3937677
## 5 0.4 0.2607076
## 6 0.5 0.2438095
## 7 0.6 0.2165605
## 8 0.7 0.1582734
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 1310
## 2 Y 143
## Popular.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 403
## 2 201
## Prediction
## Reference N Y
## N 1310 403
## Y 143 201
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.345649e-01 2.680784e-01 7.149143e-01 7.535448e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 1.497213e-28
## model_id model_method feats
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart A.nuppr.log, WordCount.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.669 0.079
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8239196 0.3 0.5137615 0.8341899
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8229644 0.8449775 0.4138559 0.7482444
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.4240506 0.7345649
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7149143 0.7535448 0.2680784if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: A.nuppr.log, WordCount.log"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00324 on full training set## Warning in myfit_mdl(model_id = "Max.cor.Y", model_method = "rpart",
## model_type = glb_model_type, : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4475
##
## CP nsplit rel error
## 1 0.003242418 0 1
##
## Node number 1: 4475 observations
## predicted class=N expected loss=0.1673743 P(node) =1
## class counts: 3726 749
## probabilities: 0.833 0.167
##
## n= 4475
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4475 749 N (0.8326257 0.1673743) *
## [1] " calling mypredict_mdl for fit:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.rpart.N
## 1 N 3726
## 2 Y 749
## Prediction
## Reference N Y
## N 3726 0
## Y 749 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.326257e-01 0.000000e+00 8.213602e-01 8.434553e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 5.097571e-01 1.800616e-164
## [1] " calling mypredict_mdl for OOB:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.rpart.N
## 1 N 1713
## 2 Y 344
## Prediction
## Reference N Y
## N 1713 0
## Y 344 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.327662e-01 0.000000e+00 8.159247e-01 8.486533e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 5.143944e-01 2.337097e-76
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.rpart rpart A.nuppr.log, WordCount.log 3
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.461 0.087 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.8232385
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8213602 0.8434553 0.1140417 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.8327662
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8159247 0.8486533 0
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.006649759 0.03033634# Used to compare vs. Interactions.High.cor.Y and/or Max.cor.Y.TmSrs
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.glm"
## [1] " indep_vars: A.nuppr.log, WordCount.log"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8402 -0.6167 -0.3941 -0.2100 3.3466
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.90112 0.36283 -13.51 <2e-16 ***
## A.nuppr.log -1.32147 0.08498 -15.55 <2e-16 ***
## WordCount.log 0.85397 0.05430 15.73 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 3392.6 on 4472 degrees of freedom
## AIC: 3398.6
##
## Number of Fisher Scoring iterations: 5
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.286753446
## 2 0.1 0.417226634
## 3 0.2 0.458198614
## 4 0.3 0.419753086
## 5 0.4 0.288224956
## 6 0.5 0.168831169
## 7 0.6 0.059925094
## 8 0.7 0.007905138
## 9 0.8 0.005319149
## 10 0.9 0.000000000
## 11 1.0 0.000000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.glm.N
## 1 N 2806
## 2 Y 253
## Popular.fctr.predict.Max.cor.Y.glm.Y
## 1 920
## 2 496
## Prediction
## Reference N Y
## N 2806 920
## Y 253 496
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.378771e-01 3.063260e-01 7.247275e-01 7.507156e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 3.164660e-84
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.286547272
## 2 0.1 0.407947020
## 3 0.2 0.441323972
## 4 0.3 0.417366947
## 5 0.4 0.319852941
## 6 0.5 0.216704289
## 7 0.6 0.074270557
## 8 0.7 0.016997167
## 9 0.8 0.005797101
## 10 0.9 0.000000000
## 11 1.0 0.000000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.glm.N
## 1 N 1280
## 2 Y 124
## Popular.fctr.predict.Max.cor.Y.glm.Y
## 1 433
## 2 220
## Prediction
## Reference N Y
## N 1280 433
## Y 124 220
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.292173e-01 2.846066e-01 7.094536e-01 7.483257e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 6.323219e-39
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.glm glm A.nuppr.log, WordCount.log 1
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.339 0.094 0.7879886
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.2 0.4581986 0.8283787
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7247275 0.7507156 0.1138834 0.7836398
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.441324 0.7292173
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7094536 0.7483257 0.2846066 3398.567
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.005782242 0.01963765if (!is.null(glb_date_vars)) {
# ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
paste0(glb_date_vars, ".day.minutes.poly.", 1:5)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
}## [1] "fitting model: Max.cor.Y.TmSrs.poly.glm"
## [1] " indep_vars: A.nuppr.log, WordCount.log, PubDate.day.minutes.poly.1, PubDate.day.minutes.poly.2, PubDate.day.minutes.poly.3, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.5"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0882 -0.5814 -0.3591 -0.1946 3.3226
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.88184 0.37216 -13.118 < 2e-16 ***
## A.nuppr.log -1.32234 0.08728 -15.151 < 2e-16 ***
## WordCount.log 0.83644 0.05577 14.997 < 2e-16 ***
## PubDate.day.minutes.poly.1 40.93832 4.51369 9.070 < 2e-16 ***
## PubDate.day.minutes.poly.2 -0.29573 4.02475 -0.073 0.941
## PubDate.day.minutes.poly.3 -5.81977 3.90004 -1.492 0.136
## PubDate.day.minutes.poly.4 30.91156 4.37647 7.063 1.63e-12 ***
## PubDate.day.minutes.poly.5 -18.25775 4.42107 -4.130 3.63e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 3239.6 on 4467 degrees of freedom
## AIC: 3255.6
##
## Number of Fisher Scoring iterations: 5
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.28675345
## 2 0.1 0.43329016
## 3 0.2 0.49787435
## 4 0.3 0.50062893
## 5 0.4 0.38189300
## 6 0.5 0.28205128
## 7 0.6 0.13974800
## 8 0.7 0.06195787
## 9 0.8 0.00795756
## 10 0.9 0.00000000
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.TmSrs.poly.glm.N
## 1 N 3283
## 2 Y 351
## Popular.fctr.predict.Max.cor.Y.TmSrs.poly.glm.Y
## 1 443
## 2 398
## Prediction
## Reference N Y
## N 3283 443
## Y 351 398
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.822569832 0.393187386 0.811055599 0.833661446 0.832625698
## AccuracyPValue McnemarPValue
## 0.965008406 0.001240203
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.286547272
## 2 0.1 0.424410541
## 3 0.2 0.500519211
## 4 0.3 0.469613260
## 5 0.4 0.387900356
## 6 0.5 0.275862069
## 7 0.6 0.133333333
## 8 0.7 0.065217391
## 9 0.8 0.005747126
## 10 0.9 0.000000000
## 11 1.0 0.000000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Max.cor.Y.TmSrs.poly.glm.N
## 1 N 1335
## 2 Y 103
## Popular.fctr.predict.Max.cor.Y.TmSrs.poly.glm.Y
## 1 378
## 2 241
## Prediction
## Reference N Y
## N 1335 378
## Y 103 241
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.661643e-01 3.637267e-01 7.472579e-01 7.843092e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 8.118863e-36
## model_id model_method
## 1 Max.cor.Y.TmSrs.poly.glm glm
## feats
## 1 A.nuppr.log, WordCount.log, PubDate.day.minutes.poly.1, PubDate.day.minutes.poly.2, PubDate.day.minutes.poly.3, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.5
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.554 0.188
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8146498 0.3 0.5006289 0.8373175
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8110556 0.8336614 0.2130159 0.8083559
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.5005192 0.7661643
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7472579 0.7843092 0.3637267 3255.592
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.004415477 0.02575099# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_vars, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.Y.glm"
## [1] " indep_vars: A.nuppr.log, WordCount.log, A.nuppr.log:A.ratio.sum.TfIdf.nwrds, A.nuppr.log:S.sum.TfIdf, A.nuppr.log:PubDate.day.minutes.poly.1, A.nuppr.log:A.ratio.nstopwrds.nwrds, A.nuppr.log:S.npnct19.log, A.nuppr.log:A.T.make, A.nuppr.log:A.npnct01.log, A.nuppr.log:H.npnct06.log, A.nuppr.log:A.T.can, A.nuppr.log:S.npnct21.log, A.nuppr.log:A.T.said, A.nuppr.log:A.npnct21.log, A.nuppr.log:A.T.one, A.nuppr.log:A.npnct07.log, A.nuppr.log:A.npnct03.log, A.nuppr.log:A.P.http, A.nuppr.log:A.npnct02.log, A.nuppr.log:A.T.appear, A.nuppr.log:A.npnct17.log, A.nuppr.log:A.npnct14.log, A.nuppr.log:A.P.year.colon, A.nuppr.log:A.T.obama, A.nuppr.log:A.npnct20.log, A.nuppr.log:A.P.first.draft, A.nuppr.log:A.T.take, A.nuppr.log:A.npnct06.log, A.nuppr.log:A.T.time, A.nuppr.log:A.T.new, A.nuppr.log:H.npnct04.log, A.nuppr.log:A.P.metropolitan.diary.colon, A.nuppr.log:H.P.today.in.politic, A.nuppr.log:H.P.what.we.are, A.nuppr.log:A.npnct12.log, A.nuppr.log:H.T.today, A.nuppr.log:S.T.year, A.nuppr.log:S.T.will, A.nuppr.log:S.T.senat, A.nuppr.log:A.T.show, A.nuppr.log:A.T.day, A.nuppr.log:H.T.first, A.nuppr.log:A.npnct28.log, A.nuppr.log:H.T.morn, A.nuppr.log:H.T.daili, A.nuppr.log:A.P.daily.clip.report, A.nuppr.log:S.T.first, A.nuppr.log:S.T.newyork, A.nuppr.log:S.T.report, A.nuppr.log:S.T.compani, A.nuppr.log:S.T.word, A.nuppr.log:S.T.newyorktim, A.nuppr.log:S.npnct13.log, A.nuppr.log:A.T.share, A.nuppr.log:S.T.articl, A.nuppr.log:H.npnct14.log, A.nuppr.log:H.T.X2015, A.nuppr.log:A.T.diari, A.nuppr.log:A.npnct04.log, A.nuppr.log:S.T.fashion, A.nuppr.log:A.T.tribun, A.nuppr.log:A.T.herald, A.nuppr.log:A.npnct15.log, A.nuppr.log:S.T.photo, A.nuppr.log:S.T.intern, A.nuppr.log:A.T.archiv, A.nuppr.log:A.P.fashion.week, A.nuppr.log:H.P.year.colon, A.nuppr.log:H.T.fashion, A.nuppr.log:S.T.week, A.nuppr.log:A.npnct11.log, A.nuppr.log:A.nstopwrds.log, A.nuppr.log:A.ndgts.log, A.nuppr.log:H.nwrds.unq.log, A.nuppr.log:H.nchrs.log, A.nuppr.log:A.nwrds.log, A.nuppr.log:S.nchrs.log, A.nuppr.log:S.nwrds.unq.log, A.nuppr.log:A.nuppr.log"
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: not plotting observations with leverage one:
## 3637, 4105
## Warning: not plotting observations with leverage one:
## 3637, 4105
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.9482 -0.5246 -0.2433 0.0000 3.3693
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) -6.051e+00 4.497e-01 -13.455
## A.nuppr.log 4.013e+00 2.312e+00 1.736
## WordCount.log 9.661e-01 6.506e-02 14.849
## `A.nuppr.log:A.ratio.sum.TfIdf.nwrds` 2.219e+00 3.509e-01 6.324
## `A.nuppr.log:S.sum.TfIdf` -1.337e-01 5.097e-02 -2.623
## `A.nuppr.log:PubDate.day.minutes.poly.1` 1.735e+01 3.456e+00 5.019
## `A.nuppr.log:A.ratio.nstopwrds.nwrds` -3.268e+00 2.806e+00 -1.165
## `A.nuppr.log:S.npnct19.log` 1.262e-01 1.761e-01 0.717
## `A.nuppr.log:A.T.make` 8.415e-02 3.847e-01 0.219
## `A.nuppr.log:A.npnct01.log` 6.330e-01 7.695e-01 0.823
## `A.nuppr.log:H.npnct06.log` 1.128e+00 2.684e-01 4.202
## `A.nuppr.log:A.T.can` -1.287e-01 4.470e-01 -0.288
## `A.nuppr.log:S.npnct21.log` NA NA NA
## `A.nuppr.log:A.T.said` 1.286e+00 4.276e-01 3.007
## `A.nuppr.log:A.npnct21.log` -1.151e+01 6.579e+03 -0.002
## `A.nuppr.log:A.T.one` -3.282e-01 3.929e-01 -0.835
## `A.nuppr.log:A.npnct07.log` -3.854e+01 8.072e+03 -0.005
## `A.nuppr.log:A.npnct03.log` -1.184e+01 4.009e+03 -0.003
## `A.nuppr.log:A.P.http` 1.503e+01 1.697e+04 0.001
## `A.nuppr.log:A.npnct02.log` -1.166e+01 9.007e+03 -0.001
## `A.nuppr.log:A.T.appear` 4.296e-02 5.216e-01 0.082
## `A.nuppr.log:A.npnct17.log` -9.641e+00 7.947e+03 -0.001
## `A.nuppr.log:A.npnct14.log` 1.791e-01 7.388e-01 0.242
## `A.nuppr.log:A.P.year.colon` 8.390e-01 2.925e+03 0.000
## `A.nuppr.log:A.T.obama` -9.294e-02 3.507e-01 -0.265
## `A.nuppr.log:A.npnct20.log` -1.590e+01 4.418e+03 -0.004
## `A.nuppr.log:A.P.first.draft` -8.740e+00 2.030e+03 -0.004
## `A.nuppr.log:A.T.take` -9.619e-01 6.265e-01 -1.535
## `A.nuppr.log:A.npnct06.log` -7.324e-01 8.450e-01 -0.867
## `A.nuppr.log:A.T.time` -6.101e-01 4.242e-01 -1.438
## `A.nuppr.log:A.T.new` -1.595e-01 4.433e-01 -0.360
## `A.nuppr.log:H.npnct04.log` -5.746e-01 5.345e-01 -1.075
## `A.nuppr.log:A.P.metropolitan.diary.colon` -5.173e+00 2.558e+00 -2.022
## `A.nuppr.log:H.P.today.in.politic` -2.058e+01 2.180e+03 -0.009
## `A.nuppr.log:H.P.what.we.are` -1.316e+01 1.762e+03 -0.007
## `A.nuppr.log:A.npnct12.log` -5.583e-02 1.127e-01 -0.495
## `A.nuppr.log:H.T.today` -9.800e-01 3.489e-01 -2.809
## `A.nuppr.log:S.T.year` -8.848e-02 5.102e-01 -0.173
## `A.nuppr.log:S.T.will` -7.176e-01 4.431e-01 -1.620
## `A.nuppr.log:S.T.senat` -1.005e-01 3.552e-01 -0.283
## `A.nuppr.log:A.T.show` -1.430e+00 6.666e-01 -2.145
## `A.nuppr.log:A.T.day` -7.569e-01 6.333e-01 -1.195
## `A.nuppr.log:H.T.first` -9.601e-01 5.546e-01 -1.731
## `A.nuppr.log:A.npnct28.log` -9.641e+00 9.730e+02 -0.010
## `A.nuppr.log:H.T.morn` 9.268e-01 1.546e+00 0.600
## `A.nuppr.log:H.T.daili` -3.658e+01 2.742e+03 -0.013
## `A.nuppr.log:A.P.daily.clip.report` 5.481e+01 5.048e+03 0.011
## `A.nuppr.log:S.T.first` 2.030e-01 5.074e-01 0.400
## `A.nuppr.log:S.T.newyork` 6.031e-01 3.777e-01 1.597
## `A.nuppr.log:S.T.report` -1.228e+00 7.245e-01 -1.695
## `A.nuppr.log:S.T.compani` -1.492e+00 6.323e-01 -2.359
## `A.nuppr.log:S.T.word` -9.583e-01 7.683e-01 -1.247
## `A.nuppr.log:S.T.newyorktim` 9.322e-02 4.834e-01 0.193
## `A.nuppr.log:S.npnct13.log` 5.686e-01 1.235e-01 4.603
## `A.nuppr.log:A.T.share` -1.477e+00 7.164e-01 -2.061
## `A.nuppr.log:S.T.articl` -5.393e-01 7.496e-01 -0.719
## `A.nuppr.log:H.npnct14.log` -2.456e+01 1.139e+03 -0.022
## `A.nuppr.log:H.T.X2015` -1.955e+01 9.337e+02 -0.021
## `A.nuppr.log:A.T.diari` 1.157e+01 5.445e+00 2.126
## `A.nuppr.log:A.npnct04.log` -1.037e+00 4.348e-01 -2.385
## `A.nuppr.log:S.T.fashion` -5.779e+01 1.940e+03 -0.030
## `A.nuppr.log:A.T.tribun` -2.516e+01 3.625e+03 -0.007
## `A.nuppr.log:A.T.herald` 3.270e+01 3.613e+03 0.009
## `A.nuppr.log:A.npnct15.log` 8.387e-02 2.535e-01 0.331
## `A.nuppr.log:S.T.photo` -2.481e+00 1.578e+00 -1.572
## `A.nuppr.log:S.T.intern` -8.699e-01 1.306e+00 -0.666
## `A.nuppr.log:A.T.archiv` -2.796e+01 1.619e+03 -0.017
## `A.nuppr.log:A.P.fashion.week` 4.068e+00 6.967e+02 0.006
## `A.nuppr.log:H.P.year.colon` -1.203e+01 1.204e+03 -0.010
## `A.nuppr.log:H.T.fashion` -1.782e+00 1.058e+00 -1.685
## `A.nuppr.log:S.T.week` -1.300e+00 5.392e-01 -2.412
## `A.nuppr.log:A.npnct11.log` -1.120e-01 7.780e-02 -1.440
## `A.nuppr.log:A.nstopwrds.log` 1.260e+00 9.356e-01 1.346
## `A.nuppr.log:A.ndgts.log` -2.302e-01 7.941e-02 -2.899
## `A.nuppr.log:H.nwrds.unq.log` -4.308e-01 2.364e-01 -1.823
## `A.nuppr.log:H.nchrs.log` -3.537e-01 2.004e-01 -1.765
## `A.nuppr.log:A.nwrds.log` 1.170e+00 1.001e+00 1.168
## `A.nuppr.log:S.nchrs.log` -1.211e+00 4.492e-01 -2.695
## `A.nuppr.log:S.nwrds.unq.log` -8.384e-01 4.385e-01 -1.912
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## A.nuppr.log 0.08259 .
## WordCount.log < 2e-16 ***
## `A.nuppr.log:A.ratio.sum.TfIdf.nwrds` 2.56e-10 ***
## `A.nuppr.log:S.sum.TfIdf` 0.00872 **
## `A.nuppr.log:PubDate.day.minutes.poly.1` 5.19e-07 ***
## `A.nuppr.log:A.ratio.nstopwrds.nwrds` 0.24415
## `A.nuppr.log:S.npnct19.log` 0.47365
## `A.nuppr.log:A.T.make` 0.82686
## `A.nuppr.log:A.npnct01.log` 0.41071
## `A.nuppr.log:H.npnct06.log` 2.64e-05 ***
## `A.nuppr.log:A.T.can` 0.77338
## `A.nuppr.log:S.npnct21.log` NA
## `A.nuppr.log:A.T.said` 0.00264 **
## `A.nuppr.log:A.npnct21.log` 0.99860
## `A.nuppr.log:A.T.one` 0.40358
## `A.nuppr.log:A.npnct07.log` 0.99619
## `A.nuppr.log:A.npnct03.log` 0.99764
## `A.nuppr.log:A.P.http` 0.99929
## `A.nuppr.log:A.npnct02.log` 0.99897
## `A.nuppr.log:A.T.appear` 0.93436
## `A.nuppr.log:A.npnct17.log` 0.99903
## `A.nuppr.log:A.npnct14.log` 0.80842
## `A.nuppr.log:A.P.year.colon` 0.99977
## `A.nuppr.log:A.T.obama` 0.79098
## `A.nuppr.log:A.npnct20.log` 0.99713
## `A.nuppr.log:A.P.first.draft` 0.99656
## `A.nuppr.log:A.T.take` 0.12467
## `A.nuppr.log:A.npnct06.log` 0.38607
## `A.nuppr.log:A.T.time` 0.15036
## `A.nuppr.log:A.T.new` 0.71898
## `A.nuppr.log:H.npnct04.log` 0.28243
## `A.nuppr.log:A.P.metropolitan.diary.colon` 0.04314 *
## `A.nuppr.log:H.P.today.in.politic` 0.99247
## `A.nuppr.log:H.P.what.we.are` 0.99404
## `A.nuppr.log:A.npnct12.log` 0.62025
## `A.nuppr.log:H.T.today` 0.00497 **
## `A.nuppr.log:S.T.year` 0.86232
## `A.nuppr.log:S.T.will` 0.10534
## `A.nuppr.log:S.T.senat` 0.77726
## `A.nuppr.log:A.T.show` 0.03195 *
## `A.nuppr.log:A.T.day` 0.23204
## `A.nuppr.log:H.T.first` 0.08344 .
## `A.nuppr.log:A.npnct28.log` 0.99209
## `A.nuppr.log:H.T.morn` 0.54874
## `A.nuppr.log:H.T.daili` 0.98936
## `A.nuppr.log:A.P.daily.clip.report` 0.99134
## `A.nuppr.log:S.T.first` 0.68915
## `A.nuppr.log:S.T.newyork` 0.11035
## `A.nuppr.log:S.T.report` 0.09015 .
## `A.nuppr.log:S.T.compani` 0.01831 *
## `A.nuppr.log:S.T.word` 0.21233
## `A.nuppr.log:S.T.newyorktim` 0.84709
## `A.nuppr.log:S.npnct13.log` 4.17e-06 ***
## `A.nuppr.log:A.T.share` 0.03927 *
## `A.nuppr.log:S.T.articl` 0.47187
## `A.nuppr.log:H.npnct14.log` 0.98280
## `A.nuppr.log:H.T.X2015` 0.98330
## `A.nuppr.log:A.T.diari` 0.03354 *
## `A.nuppr.log:A.npnct04.log` 0.01709 *
## `A.nuppr.log:S.T.fashion` 0.97623
## `A.nuppr.log:A.T.tribun` 0.99446
## `A.nuppr.log:A.T.herald` 0.99278
## `A.nuppr.log:A.npnct15.log` 0.74080
## `A.nuppr.log:S.T.photo` 0.11586
## `A.nuppr.log:S.T.intern` 0.50550
## `A.nuppr.log:A.T.archiv` 0.98622
## `A.nuppr.log:A.P.fashion.week` 0.99534
## `A.nuppr.log:H.P.year.colon` 0.99202
## `A.nuppr.log:H.T.fashion` 0.09202 .
## `A.nuppr.log:S.T.week` 0.01588 *
## `A.nuppr.log:A.npnct11.log` 0.14986
## `A.nuppr.log:A.nstopwrds.log` 0.17815
## `A.nuppr.log:A.ndgts.log` 0.00374 **
## `A.nuppr.log:H.nwrds.unq.log` 0.06836 .
## `A.nuppr.log:H.nchrs.log` 0.07754 .
## `A.nuppr.log:A.nwrds.log` 0.24266
## `A.nuppr.log:S.nchrs.log` 0.00704 **
## `A.nuppr.log:S.nwrds.unq.log` 0.05588 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 2702.0 on 4397 degrees of freedom
## AIC: 2858
##
## Number of Fisher Scoring iterations: 19
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.28675345
## 2 0.1 0.49281609
## 3 0.2 0.57239382
## 4 0.3 0.59124088
## 5 0.4 0.56189770
## 6 0.5 0.48317515
## 7 0.6 0.39223301
## 8 0.7 0.29126214
## 9 0.8 0.18461538
## 10 0.9 0.08860759
## 11 1.0 0.00000000## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 3317
## 2 Y 263
## Popular.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 409
## 2 486
## Prediction
## Reference N Y
## N 3317 409
## Y 263 486
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.498324e-01 5.001488e-01 8.390261e-01 8.601799e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 9.594273e-04 2.225418e-08
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.4888535
## 3 0.2 0.5557895
## 4 0.3 0.5565670
## 5 0.4 0.5356037
## 6 0.5 0.4395604
## 7 0.6 0.3815514
## 8 0.7 0.3087558
## 9 0.8 0.2035623
## 10 0.9 0.1185984
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 1502
## 2 Y 130
## Popular.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 211
## 2 214
## Prediction
## Reference N Y
## N 1502 211
## Y 130 214
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8342245989 0.4560112856 0.8174373220 0.8500532876 0.8327661643
## AccuracyPValue McnemarPValue
## 0.4438238782 0.0000147598
## model_id model_method
## 1 Interact.High.cor.Y.glm glm
## feats
## 1 A.nuppr.log, WordCount.log, A.nuppr.log:A.ratio.sum.TfIdf.nwrds, A.nuppr.log:S.sum.TfIdf, A.nuppr.log:PubDate.day.minutes.poly.1, A.nuppr.log:A.ratio.nstopwrds.nwrds, A.nuppr.log:S.npnct19.log, A.nuppr.log:A.T.make, A.nuppr.log:A.npnct01.log, A.nuppr.log:H.npnct06.log, A.nuppr.log:A.T.can, A.nuppr.log:S.npnct21.log, A.nuppr.log:A.T.said, A.nuppr.log:A.npnct21.log, A.nuppr.log:A.T.one, A.nuppr.log:A.npnct07.log, A.nuppr.log:A.npnct03.log, A.nuppr.log:A.P.http, A.nuppr.log:A.npnct02.log, A.nuppr.log:A.T.appear, A.nuppr.log:A.npnct17.log, A.nuppr.log:A.npnct14.log, A.nuppr.log:A.P.year.colon, A.nuppr.log:A.T.obama, A.nuppr.log:A.npnct20.log, A.nuppr.log:A.P.first.draft, A.nuppr.log:A.T.take, A.nuppr.log:A.npnct06.log, A.nuppr.log:A.T.time, A.nuppr.log:A.T.new, A.nuppr.log:H.npnct04.log, A.nuppr.log:A.P.metropolitan.diary.colon, A.nuppr.log:H.P.today.in.politic, A.nuppr.log:H.P.what.we.are, A.nuppr.log:A.npnct12.log, A.nuppr.log:H.T.today, A.nuppr.log:S.T.year, A.nuppr.log:S.T.will, A.nuppr.log:S.T.senat, A.nuppr.log:A.T.show, A.nuppr.log:A.T.day, A.nuppr.log:H.T.first, A.nuppr.log:A.npnct28.log, A.nuppr.log:H.T.morn, A.nuppr.log:H.T.daili, A.nuppr.log:A.P.daily.clip.report, A.nuppr.log:S.T.first, A.nuppr.log:S.T.newyork, A.nuppr.log:S.T.report, A.nuppr.log:S.T.compani, A.nuppr.log:S.T.word, A.nuppr.log:S.T.newyorktim, A.nuppr.log:S.npnct13.log, A.nuppr.log:A.T.share, A.nuppr.log:S.T.articl, A.nuppr.log:H.npnct14.log, A.nuppr.log:H.T.X2015, A.nuppr.log:A.T.diari, A.nuppr.log:A.npnct04.log, A.nuppr.log:S.T.fashion, A.nuppr.log:A.T.tribun, A.nuppr.log:A.T.herald, A.nuppr.log:A.npnct15.log, A.nuppr.log:S.T.photo, A.nuppr.log:S.T.intern, A.nuppr.log:A.T.archiv, A.nuppr.log:A.P.fashion.week, A.nuppr.log:H.P.year.colon, A.nuppr.log:H.T.fashion, A.nuppr.log:S.T.week, A.nuppr.log:A.npnct11.log, A.nuppr.log:A.nstopwrds.log, A.nuppr.log:A.ndgts.log, A.nuppr.log:H.nwrds.unq.log, A.nuppr.log:H.nchrs.log, A.nuppr.log:A.nwrds.log, A.nuppr.log:S.nchrs.log, A.nuppr.log:S.nwrds.unq.log, A.nuppr.log:A.nuppr.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 4.557 1.862
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8805672 0.3 0.5912409 0.8643586
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8390261 0.8601799 0.4117899 0.8577584
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.556567 0.8342246
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.8174373 0.8500533 0.4560113 2857.963
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.004259063 0.02653463# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) & !myNearZV &
(exclude.as.feat != 1))[, "id"]
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":", feat))
return(vars_vctr)
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.glm"
## [1] " indep_vars: WordCount.log, A.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, H.npnct19.log, A.ratio.nstopwrds.nwrds, PubDate.wkend, H.P.recap.colon, H.P.quandary, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, H.P.no.comment.colon, S.npnct19.log, H.P.facts.figures, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.P.readers.respond, A.T.make, H.ratio.nstopwrds.nwrds, H.T.get, H.npnct06.log, A.npnct01.log, A.T.can, PubDate.day.minutes.poly.3, H.T.ebola, H.npnct01.log, A.T.said, H.T.make, H.npnct11.log, A.T.one, H.P.s.notebook, H.T.take, A.npnct16.log, S.npnct16.log, A.T.presid, S.T.presid, S.npnct08.log, A.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, H.P.friday.night.music, H.T.say, H.T.obama, H.T.bank, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, H.npnct12.log, A.npnct03.log, H.T.big, A.P.year.colon, A.T.obama, A.npnct20.log, H.npnct02.log, H.T.test, S.npnct14.log, A.P.first.draft, H.P.on.this.day, A.T.take, A.npnct06.log, A.npnct14.log, A.T.time, H.T.newyorktim, H.npnct13.log, H.T.deal, A.T.new, A.P.metropolitan.diary.colon, H.P.verbatim.colon, H.T.china, H.T.art, PubDate.minute.fctr, S.T.year, A.npnct12.log, H.P.what.we.are, S.T.will, A.T.appear, PubDate.wkday.fctr, H.T.pictur, H.T.new, S.T.senat, A.T.show, H.P.today.in.smallbusiness, A.T.day, H.T.news, S.T.first, H.T.first, H.T.X2014, S.T.newyork, S.T.report, S.T.compani, S.T.word, H.T.morn, H.T.busi, S.T.newyorktim, A.T.share, H.npnct04.log, S.npnct13.log, S.T.articl, H.T.newyork, PubDate.day.minutes.poly.5, H.T.today, H.T.day, A.npnct04.log, H.T.daili, H.T.week, S.T.photo, A.npnct15.log, H.P.year.colon, H.T.fashion, H.npnct15.log, S.T.fashion, S.T.week, H.nstopwrds.log, H.npnct28.log, A.npnct11.log, H.ndgts.log, A.ndgts.log, H.nwrds.unq.log, A.nuppr.log, myCategory.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.9321 -0.2697 -0.0821 0.0000 3.3712
##
## Coefficients: (315 not defined because of singularities)
## Estimate
## (Intercept) -6.769e+00
## WordCount.log 1.238e+00
## A.ratio.sum.TfIdf.nwrds 9.886e-01
## H.ratio.sum.TfIdf.nwrds 1.721e-01
## `myCategory.fctr#Multimedia#` -1.832e+00
## `myCategory.fctr#Opinion#Room For Debate` -5.077e+00
## `myCategory.fctr#Opinion#The Public Editor` 4.702e+00
## `myCategory.fctr#U.S.#Education` -1.958e+01
## `myCategory.fctrBusiness#Business Day#Dealbook` -3.137e-01
## `myCategory.fctrBusiness#Business Day#Small Business` -2.089e+00
## `myCategory.fctrBusiness#Crosswords/Games#` 2.952e+00
## `myCategory.fctrBusiness#Technology#` 6.490e-02
## `myCategory.fctrCulture#Arts#` -3.290e-01
## `myCategory.fctrForeign#World#` -1.585e+01
## `myCategory.fctrForeign#World#Asia Pacific` -1.398e+00
## `myCategory.fctrMetro#N.Y. / Region#` 9.684e-01
## myCategory.fctrmyOther -1.829e+01
## `myCategory.fctrOpEd#Opinion#` 3.193e+00
## `myCategory.fctrScience#Health#` 2.773e+00
## `myCategory.fctrStyles##Fashion` -1.996e+01
## `myCategory.fctrStyles#U.S.#` 2.086e+00
## `myCategory.fctrTravel#Travel#` -1.632e+00
## `myCategory.fctrTStyle##` -1.951e+00
## PubDate.day.minutes.poly.1 6.898e+00
## H.sum.TfIdf -6.992e-02
## S.sum.TfIdf -2.330e-01
## H.npnct19.log 1.396e+00
## A.ratio.nstopwrds.nwrds 2.423e+00
## PubDate.wkend -1.461e-01
## H.P.recap.colon -1.943e+01
## H.P.quandary 2.266e+01
## PubDate.day.minutes.poly.4 1.848e+00
## PubDate.day.minutes.poly.2 2.046e+01
## H.P.no.comment.colon 2.908e+00
## S.npnct19.log 1.618e+00
## H.P.facts.figures 9.380e-01
## H.npnct08.log 1.152e+00
## PubDate.last10.log 1.056e-01
## PubDate.last1.log -5.083e-02
## H.P.readers.respond 7.321e+00
## A.T.make -1.187e+00
## H.ratio.nstopwrds.nwrds -1.656e+00
## H.T.get 6.076e-01
## H.npnct06.log 7.749e-01
## A.npnct01.log 5.469e-01
## A.T.can -1.391e+00
## PubDate.day.minutes.poly.3 7.902e+00
## H.T.ebola -3.902e-01
## H.npnct01.log -1.930e+00
## A.T.said 6.350e-01
## H.T.make -2.248e-01
## H.npnct11.log 4.470e-01
## A.T.one -3.980e-01
## H.P.s.notebook -1.732e+01
## H.T.take -9.824e-02
## A.npnct16.log -2.099e-01
## S.npnct16.log NA
## A.T.presid 1.631e+03
## S.T.presid -1.630e+03
## S.npnct08.log 9.933e-01
## A.npnct08.log NA
## PubDate.last100.log 1.650e-02
## .rnorm -8.828e-02
## H.npnct05.log -2.556e+01
## H.P.friday.night.music -2.346e+00
## H.T.say -7.790e-01
## H.T.obama 2.007e-01
## H.T.bank 1.162e+00
## `PubDate.date.fctr(7,13]` 2.951e-02
## `PubDate.date.fctr(13,19]` -1.359e-01
## `PubDate.date.fctr(19,25]` -1.045e-01
## `PubDate.date.fctr(25,31]` 1.810e-01
## `PubDate.second.fctr(14.8,29.5]` 6.794e-02
## `PubDate.second.fctr(29.5,44.2]` -3.073e-02
## `PubDate.second.fctr(44.2,59.1]` -2.645e-01
## H.npnct07.log 2.064e-01
## A.npnct07.log -2.788e+01
## H.npnct12.log 5.001e-01
## A.npnct03.log -3.128e+01
## H.T.big -7.910e-01
## A.P.year.colon 1.749e+00
## A.T.obama 3.001e-01
## A.npnct20.log -2.524e+01
## H.npnct02.log -1.857e+01
## H.T.test 2.876e-01
## S.npnct14.log 9.542e+00
## A.P.first.draft -1.525e+01
## H.P.on.this.day -1.508e+01
## A.T.take -9.944e-01
## A.npnct06.log 1.670e-01
## A.npnct14.log -9.230e+00
## A.T.time -1.198e+00
## H.T.newyorktim -4.682e-01
## H.npnct13.log 1.666e-02
## H.T.deal -2.314e+01
## A.T.new 4.562e-01
## A.P.metropolitan.diary.colon -7.563e-01
## H.P.verbatim.colon -1.534e+01
## H.T.china -1.619e+00
## H.T.art -1.625e+00
## `PubDate.minute.fctr(14.8,29.5]` -1.100e-01
## `PubDate.minute.fctr(29.5,44.2]` -1.782e-01
## `PubDate.minute.fctr(44.2,59.1]` 1.039e-01
## S.T.year 6.296e-01
## A.npnct12.log -9.038e-02
## H.P.what.we.are -2.078e+01
## S.T.will -4.697e-01
## A.T.appear -1.796e-01
## PubDate.wkday.fctr1 -4.154e-01
## PubDate.wkday.fctr2 -9.400e-01
## PubDate.wkday.fctr3 -4.557e-01
## PubDate.wkday.fctr4 -7.177e-01
## PubDate.wkday.fctr5 -5.423e-01
## PubDate.wkday.fctr6 -7.841e-01
## H.T.pictur -3.709e-03
## H.T.new -2.366e-01
## S.T.senat 1.180e+00
## A.T.show -1.117e+00
## H.P.today.in.smallbusiness -1.421e+01
## A.T.day 1.131e-01
## H.T.news -7.898e-01
## S.T.first 1.405e+00
## H.T.first -4.030e-01
## H.T.X2014 -5.932e-01
## S.T.newyork 2.311e+00
## S.T.report -7.652e-01
## S.T.compani -7.999e-01
## S.T.word -4.685e-01
## H.T.morn -1.847e+00
## H.T.busi -7.572e-01
## S.T.newyorktim 2.118e+00
## A.T.share -2.539e+00
## H.npnct04.log -2.435e+00
## S.npnct13.log 8.054e-01
## S.T.articl -4.145e+00
## H.T.newyork -4.155e-01
## PubDate.day.minutes.poly.5 -8.207e+00
## H.T.today -1.172e+00
## H.T.day -9.955e-01
## A.npnct04.log -1.181e+00
## H.T.daili -2.349e+01
## H.T.week -8.980e-01
## S.T.photo -3.326e+00
## A.npnct15.log 1.613e-01
## H.P.year.colon -1.515e+01
## H.T.fashion 1.193e+00
## H.npnct15.log -1.091e+00
## S.T.fashion -5.181e+01
## S.T.week 3.411e-01
## H.nstopwrds.log 1.869e-01
## H.npnct28.log -1.115e+00
## A.npnct11.log -2.080e-01
## H.ndgts.log 4.872e-01
## A.ndgts.log -2.783e-01
## H.nwrds.unq.log -9.582e-01
## A.nuppr.log -4.376e-01
## `myCategory.fctr##:.clusterid.fctr2` -2.164e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 4.438e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 3.226e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` -8.097e-02
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` -2.221e-02
## `myCategory.fctrScience#Health#:.clusterid.fctr2` -3.573e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` -9.258e-02
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` -7.360e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
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## `myCategory.fctrCulture#Arts#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18` NA
## `myCategory.fctrmyOther:.clusterid.fctr18` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr18` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18` NA
## `myCategory.fctrTStyle##:.clusterid.fctr18` NA
## `myCategory.fctr##:.clusterid.fctr19` -1.141e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19` NA
## `myCategory.fctrmyOther:.clusterid.fctr19` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr19` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
## `myCategory.fctrTStyle##:.clusterid.fctr19` NA
## `myCategory.fctr##:.clusterid.fctr20` -1.897e+01
## `myCategory.fctr#Multimedia#:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20` NA
## `myCategory.fctrmyOther:.clusterid.fctr20` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr20` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
## Std. Error
## (Intercept) 2.225e+00
## WordCount.log 1.067e-01
## A.ratio.sum.TfIdf.nwrds 2.875e-01
## H.ratio.sum.TfIdf.nwrds 1.661e-01
## `myCategory.fctr#Multimedia#` 8.610e-01
## `myCategory.fctr#Opinion#Room For Debate` 8.652e-01
## `myCategory.fctr#Opinion#The Public Editor` 1.558e+00
## `myCategory.fctr#U.S.#Education` 9.074e+02
## `myCategory.fctrBusiness#Business Day#Dealbook` 4.142e-01
## `myCategory.fctrBusiness#Business Day#Small Business` 7.286e-01
## `myCategory.fctrBusiness#Crosswords/Games#` 5.684e-01
## `myCategory.fctrBusiness#Technology#` 7.861e-01
## `myCategory.fctrCulture#Arts#` 6.058e-01
## `myCategory.fctrForeign#World#` 1.250e+03
## `myCategory.fctrForeign#World#Asia Pacific` 6.970e-01
## `myCategory.fctrMetro#N.Y. / Region#` 6.156e-01
## myCategory.fctrmyOther 2.963e+03
## `myCategory.fctrOpEd#Opinion#` 3.780e-01
## `myCategory.fctrScience#Health#` 4.708e-01
## `myCategory.fctrStyles##Fashion` 1.490e+03
## `myCategory.fctrStyles#U.S.#` 4.838e-01
## `myCategory.fctrTravel#Travel#` 1.080e+00
## `myCategory.fctrTStyle##` 5.668e-01
## PubDate.day.minutes.poly.1 7.920e+00
## H.sum.TfIdf 8.403e-02
## S.sum.TfIdf 9.954e-02
## H.npnct19.log 3.528e-01
## A.ratio.nstopwrds.nwrds 8.722e-01
## PubDate.wkend 4.923e-01
## H.P.recap.colon 8.909e+03
## H.P.quandary 6.833e+03
## PubDate.day.minutes.poly.4 7.345e+00
## PubDate.day.minutes.poly.2 7.674e+00
## H.P.no.comment.colon 1.234e+00
## S.npnct19.log 3.552e-01
## H.P.facts.figures 1.522e+00
## H.npnct08.log 4.858e-01
## PubDate.last10.log 1.418e-01
## PubDate.last1.log 4.729e-02
## H.P.readers.respond 1.122e+00
## A.T.make 7.017e-01
## H.ratio.nstopwrds.nwrds 2.068e+00
## H.T.get 4.330e-01
## H.npnct06.log 6.483e-01
## A.npnct01.log 1.909e+00
## A.T.can 9.053e-01
## PubDate.day.minutes.poly.3 7.023e+00
## H.T.ebola 4.144e-01
## H.npnct01.log 1.415e+00
## A.T.said 9.385e-01
## H.T.make 4.002e-01
## H.npnct11.log 2.227e-01
## A.T.one 7.732e-01
## H.P.s.notebook 8.700e+03
## H.T.take 5.277e-01
## A.npnct16.log 1.363e+00
## S.npnct16.log NA
## A.T.presid 2.179e+05
## S.T.presid 2.179e+05
## S.npnct08.log 6.541e-01
## A.npnct08.log NA
## PubDate.last100.log 4.788e-02
## .rnorm 6.773e-02
## H.npnct05.log 1.024e+04
## H.P.friday.night.music 1.326e+00
## H.T.say 5.024e-01
## H.T.obama 6.983e-01
## H.T.bank 8.277e-01
## `PubDate.date.fctr(7,13]` 2.119e-01
## `PubDate.date.fctr(13,19]` 2.100e-01
## `PubDate.date.fctr(19,25]` 2.056e-01
## `PubDate.date.fctr(25,31]` 2.218e-01
## `PubDate.second.fctr(14.8,29.5]` 1.887e-01
## `PubDate.second.fctr(29.5,44.2]` 1.849e-01
## `PubDate.second.fctr(44.2,59.1]` 1.910e-01
## H.npnct07.log 2.168e-01
## A.npnct07.log 1.050e+04
## H.npnct12.log 3.462e-01
## A.npnct03.log 8.688e+03
## H.T.big 7.071e-01
## A.P.year.colon 5.171e+03
## A.T.obama 1.090e+00
## A.npnct20.log 7.615e+03
## H.npnct02.log 5.049e+03
## H.T.test 9.118e-01
## S.npnct14.log 3.544e+03
## A.P.first.draft 4.540e+03
## H.P.on.this.day 5.497e+03
## A.T.take 1.202e+00
## A.npnct06.log 1.647e+00
## A.npnct14.log 3.544e+03
## A.T.time 1.036e+00
## H.T.newyorktim 8.500e-01
## H.npnct13.log 2.015e-01
## H.T.deal 2.527e+03
## A.T.new 9.161e-01
## A.P.metropolitan.diary.colon 8.930e-01
## H.P.verbatim.colon 3.522e+03
## H.T.china 1.151e+00
## H.T.art 1.025e+00
## `PubDate.minute.fctr(14.8,29.5]` 1.967e-01
## `PubDate.minute.fctr(29.5,44.2]` 1.915e-01
## `PubDate.minute.fctr(44.2,59.1]` 1.988e-01
## S.T.year 1.073e+00
## A.npnct12.log 2.202e-01
## H.P.what.we.are 2.735e+03
## S.T.will 9.303e-01
## A.T.appear 1.299e+00
## PubDate.wkday.fctr1 5.784e-01
## PubDate.wkday.fctr2 6.315e-01
## PubDate.wkday.fctr3 6.212e-01
## PubDate.wkday.fctr4 6.151e-01
## PubDate.wkday.fctr5 6.218e-01
## PubDate.wkday.fctr6 5.421e-01
## H.T.pictur 6.406e-01
## H.T.new 5.723e-01
## S.T.senat 1.235e+00
## A.T.show 1.290e+00
## H.P.today.in.smallbusiness 2.884e+03
## A.T.day 1.099e+00
## H.T.news 8.252e-01
## S.T.first 1.240e+00
## H.T.first 1.242e+00
## H.T.X2014 1.068e+00
## S.T.newyork 1.092e+00
## S.T.report 1.356e+00
## S.T.compani 1.297e+00
## S.T.word 1.501e+00
## H.T.morn 1.129e+00
## H.T.busi 7.818e-01
## S.T.newyorktim 1.663e+00
## A.T.share 1.255e+00
## H.npnct04.log 1.201e+00
## S.npnct13.log 2.775e-01
## S.T.articl 2.433e+00
## H.T.newyork 5.838e-01
## PubDate.day.minutes.poly.5 6.678e+00
## H.T.today 6.336e-01
## H.T.day 7.337e-01
## A.npnct04.log 7.216e-01
## H.T.daili 1.606e+03
## H.T.week 7.451e-01
## S.T.photo 2.365e+00
## A.npnct15.log 5.675e-01
## H.P.year.colon 1.253e+03
## H.T.fashion 1.628e+00
## H.npnct15.log 3.782e-01
## S.T.fashion 2.425e+03
## S.T.week 9.122e-01
## H.nstopwrds.log 4.461e-01
## H.npnct28.log 2.119e+00
## A.npnct11.log 1.514e-01
## H.ndgts.log 2.755e-01
## A.ndgts.log 1.688e-01
## H.nwrds.unq.log 3.982e-01
## A.nuppr.log 1.668e-01
## `myCategory.fctr##:.clusterid.fctr2` 1.187e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 5.991e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 8.473e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 9.697e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 6.128e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 6.445e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 5.863e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 6.872e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 8.302e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 8.417e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 2.623e+03
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 6.165e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 6.405e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 6.757e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 1.657e+03
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 1.056e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 8.475e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 8.909e+03
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 8.655e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 6.849e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 7.757e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` 9.174e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 6.463e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 1.035e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 1.206e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 6.608e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 7.536e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 9.225e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 3.873e+03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 2.683e+03
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 7.555e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` 1.028e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 7.530e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 1.026e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 1.196e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.084e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 6.277e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 7.912e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` 1.308e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 3.375e+03
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 8.816e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` 8.037e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 1.339e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 3.316e+03
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` 2.139e+03
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 6.872e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 4.944e+03
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` 7.005e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 3.240e+03
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 4.374e+03
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` 1.323e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 1.115e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 1.240e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` 8.198e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 1.232e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 1.500e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` 1.381e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 3.731e+03
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 4.635e+03
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 6.864e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 1.030e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 1.194e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 1.285e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` 9.030e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr##:.clusterid.fctr18` 1.451e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr18` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr18` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18` NA
## `myCategory.fctrmyOther:.clusterid.fctr18` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr18` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18` NA
## `myCategory.fctrTStyle##:.clusterid.fctr18` NA
## `myCategory.fctr##:.clusterid.fctr19` 1.778e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19` NA
## `myCategory.fctrmyOther:.clusterid.fctr19` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr19` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
## `myCategory.fctrTStyle##:.clusterid.fctr19` NA
## `myCategory.fctr##:.clusterid.fctr20` 5.328e+03
## `myCategory.fctr#Multimedia#:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20` NA
## `myCategory.fctrmyOther:.clusterid.fctr20` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr20` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
## z value
## (Intercept) -3.043
## WordCount.log 11.599
## A.ratio.sum.TfIdf.nwrds 3.439
## H.ratio.sum.TfIdf.nwrds 1.036
## `myCategory.fctr#Multimedia#` -2.128
## `myCategory.fctr#Opinion#Room For Debate` -5.868
## `myCategory.fctr#Opinion#The Public Editor` 3.017
## `myCategory.fctr#U.S.#Education` -0.022
## `myCategory.fctrBusiness#Business Day#Dealbook` -0.757
## `myCategory.fctrBusiness#Business Day#Small Business` -2.868
## `myCategory.fctrBusiness#Crosswords/Games#` 5.193
## `myCategory.fctrBusiness#Technology#` 0.083
## `myCategory.fctrCulture#Arts#` -0.543
## `myCategory.fctrForeign#World#` -0.013
## `myCategory.fctrForeign#World#Asia Pacific` -2.006
## `myCategory.fctrMetro#N.Y. / Region#` 1.573
## myCategory.fctrmyOther -0.006
## `myCategory.fctrOpEd#Opinion#` 8.446
## `myCategory.fctrScience#Health#` 5.889
## `myCategory.fctrStyles##Fashion` -0.013
## `myCategory.fctrStyles#U.S.#` 4.312
## `myCategory.fctrTravel#Travel#` -1.512
## `myCategory.fctrTStyle##` -3.443
## PubDate.day.minutes.poly.1 0.871
## H.sum.TfIdf -0.832
## S.sum.TfIdf -2.341
## H.npnct19.log 3.958
## A.ratio.nstopwrds.nwrds 2.778
## PubDate.wkend -0.297
## H.P.recap.colon -0.002
## H.P.quandary 0.003
## PubDate.day.minutes.poly.4 0.252
## PubDate.day.minutes.poly.2 2.666
## H.P.no.comment.colon 2.356
## S.npnct19.log 4.556
## H.P.facts.figures 0.616
## H.npnct08.log 2.372
## PubDate.last10.log 0.745
## PubDate.last1.log -1.075
## H.P.readers.respond 6.525
## A.T.make -1.691
## H.ratio.nstopwrds.nwrds -0.801
## H.T.get 1.403
## H.npnct06.log 1.195
## A.npnct01.log 0.286
## A.T.can -1.537
## PubDate.day.minutes.poly.3 1.125
## H.T.ebola -0.942
## H.npnct01.log -1.364
## A.T.said 0.677
## H.T.make -0.562
## H.npnct11.log 2.007
## A.T.one -0.515
## H.P.s.notebook -0.002
## H.T.take -0.186
## A.npnct16.log -0.154
## S.npnct16.log NA
## A.T.presid 0.007
## S.T.presid -0.007
## S.npnct08.log 1.519
## A.npnct08.log NA
## PubDate.last100.log 0.345
## .rnorm -1.303
## H.npnct05.log -0.002
## H.P.friday.night.music -1.769
## H.T.say -1.551
## H.T.obama 0.287
## H.T.bank 1.404
## `PubDate.date.fctr(7,13]` 0.139
## `PubDate.date.fctr(13,19]` -0.647
## `PubDate.date.fctr(19,25]` -0.508
## `PubDate.date.fctr(25,31]` 0.816
## `PubDate.second.fctr(14.8,29.5]` 0.360
## `PubDate.second.fctr(29.5,44.2]` -0.166
## `PubDate.second.fctr(44.2,59.1]` -1.385
## H.npnct07.log 0.952
## A.npnct07.log -0.003
## H.npnct12.log 1.445
## A.npnct03.log -0.004
## H.T.big -1.119
## A.P.year.colon 0.000
## A.T.obama 0.275
## A.npnct20.log -0.003
## H.npnct02.log -0.004
## H.T.test 0.315
## S.npnct14.log 0.003
## A.P.first.draft -0.003
## H.P.on.this.day -0.003
## A.T.take -0.827
## A.npnct06.log 0.101
## A.npnct14.log -0.003
## A.T.time -1.157
## H.T.newyorktim -0.551
## H.npnct13.log 0.083
## H.T.deal -0.009
## A.T.new 0.498
## A.P.metropolitan.diary.colon -0.847
## H.P.verbatim.colon -0.004
## H.T.china -1.407
## H.T.art -1.585
## `PubDate.minute.fctr(14.8,29.5]` -0.559
## `PubDate.minute.fctr(29.5,44.2]` -0.930
## `PubDate.minute.fctr(44.2,59.1]` 0.523
## S.T.year 0.587
## A.npnct12.log -0.411
## H.P.what.we.are -0.008
## S.T.will -0.505
## A.T.appear -0.138
## PubDate.wkday.fctr1 -0.718
## PubDate.wkday.fctr2 -1.489
## PubDate.wkday.fctr3 -0.734
## PubDate.wkday.fctr4 -1.167
## PubDate.wkday.fctr5 -0.872
## PubDate.wkday.fctr6 -1.446
## H.T.pictur -0.006
## H.T.new -0.414
## S.T.senat 0.955
## A.T.show -0.866
## H.P.today.in.smallbusiness -0.005
## A.T.day 0.103
## H.T.news -0.957
## S.T.first 1.133
## H.T.first -0.324
## H.T.X2014 -0.555
## S.T.newyork 2.117
## S.T.report -0.564
## S.T.compani -0.617
## S.T.word -0.312
## H.T.morn -1.636
## H.T.busi -0.968
## S.T.newyorktim 1.273
## A.T.share -2.024
## H.npnct04.log -2.027
## S.npnct13.log 2.903
## S.T.articl -1.703
## H.T.newyork -0.712
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
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## `myCategory.fctrCulture#Arts#:.clusterid.fctr18` NA
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr18` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19` NA
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
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## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
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## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
## Pr(>|z|)
## (Intercept) 0.002343
## WordCount.log < 2e-16
## A.ratio.sum.TfIdf.nwrds 0.000584
## H.ratio.sum.TfIdf.nwrds 0.300061
## `myCategory.fctr#Multimedia#` 0.033340
## `myCategory.fctr#Opinion#Room For Debate` 4.41e-09
## `myCategory.fctr#Opinion#The Public Editor` 0.002553
## `myCategory.fctr#U.S.#Education` 0.982786
## `myCategory.fctrBusiness#Business Day#Dealbook` 0.448866
## `myCategory.fctrBusiness#Business Day#Small Business` 0.004134
## `myCategory.fctrBusiness#Crosswords/Games#` 2.07e-07
## `myCategory.fctrBusiness#Technology#` 0.934206
## `myCategory.fctrCulture#Arts#` 0.587017
## `myCategory.fctrForeign#World#` 0.989884
## `myCategory.fctrForeign#World#Asia Pacific` 0.044892
## `myCategory.fctrMetro#N.Y. / Region#` 0.115670
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## `myCategory.fctrOpEd#Opinion#` < 2e-16
## `myCategory.fctrScience#Health#` 3.88e-09
## `myCategory.fctrStyles##Fashion` 0.989309
## `myCategory.fctrStyles#U.S.#` 1.62e-05
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## H.sum.TfIdf 0.405351
## S.sum.TfIdf 0.019223
## H.npnct19.log 7.55e-05
## A.ratio.nstopwrds.nwrds 0.005473
## PubDate.wkend 0.766663
## H.P.recap.colon 0.998260
## H.P.quandary 0.997354
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## PubDate.day.minutes.poly.2 0.007680
## H.P.no.comment.colon 0.018472
## S.npnct19.log 5.22e-06
## H.P.facts.figures 0.537713
## H.npnct08.log 0.017670
## PubDate.last10.log 0.456545
## PubDate.last1.log 0.282391
## H.P.readers.respond 6.80e-11
## A.T.make 0.090820
## H.ratio.nstopwrds.nwrds 0.423258
## H.T.get 0.160530
## H.npnct06.log 0.231940
## A.npnct01.log 0.774523
## A.T.can 0.124368
## PubDate.day.minutes.poly.3 0.260472
## H.T.ebola 0.346332
## H.npnct01.log 0.172476
## A.T.said 0.498683
## H.T.make 0.574275
## H.npnct11.log 0.044718
## A.T.one 0.606741
## H.P.s.notebook 0.998412
## H.T.take 0.852302
## A.npnct16.log 0.877632
## S.npnct16.log NA
## A.T.presid 0.994029
## S.T.presid 0.994032
## S.npnct08.log 0.128873
## A.npnct08.log NA
## PubDate.last100.log 0.730451
## .rnorm 0.192437
## H.npnct05.log 0.998009
## H.P.friday.night.music 0.076835
## H.T.say 0.121011
## H.T.obama 0.773781
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## `PubDate.date.fctr(7,13]` 0.889252
## `PubDate.date.fctr(13,19]` 0.517422
## `PubDate.date.fctr(19,25]` 0.611188
## `PubDate.date.fctr(25,31]` 0.414521
## `PubDate.second.fctr(14.8,29.5]` 0.718819
## `PubDate.second.fctr(29.5,44.2]` 0.868025
## `PubDate.second.fctr(44.2,59.1]` 0.166080
## H.npnct07.log 0.341131
## A.npnct07.log 0.997882
## H.npnct12.log 0.148596
## A.npnct03.log 0.997127
## H.T.big 0.263329
## A.P.year.colon 0.999730
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## H.npnct02.log 0.997065
## H.T.test 0.752425
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## H.P.on.this.day 0.997811
## A.T.take 0.408075
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## A.npnct14.log 0.997922
## A.T.time 0.247321
## H.T.newyorktim 0.581773
## H.npnct13.log 0.934116
## H.T.deal 0.992692
## A.T.new 0.618539
## A.P.metropolitan.diary.colon 0.397071
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## `PubDate.minute.fctr(29.5,44.2]` 0.352234
## `PubDate.minute.fctr(44.2,59.1]` 0.601101
## S.T.year 0.557227
## A.npnct12.log 0.681423
## H.P.what.we.are 0.993938
## S.T.will 0.613635
## A.T.appear 0.889984
## PubDate.wkday.fctr1 0.472666
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## PubDate.wkday.fctr5 0.383093
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## H.T.pictur 0.995381
## H.T.new 0.679238
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## A.T.day 0.917986
## H.T.news 0.338509
## S.T.first 0.257027
## H.T.first 0.745611
## H.T.X2014 0.578580
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## S.T.report 0.572465
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## H.T.busi 0.332801
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## S.T.articl 0.088539
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## H.T.today 0.064263
## H.T.day 0.174808
## A.npnct04.log 0.101746
## H.T.daili 0.988332
## H.T.week 0.228166
## S.T.photo 0.159551
## A.npnct15.log 0.776229
## H.P.year.colon 0.990355
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## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.458821
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
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## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 0.933456
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
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## `myCategory.fctrScience#Health#:.clusterid.fctr2` 0.579347
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 0.874540
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 0.284170
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
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## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 0.128004
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 0.994635
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
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## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 0.964343
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## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
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## `myCategory.fctr##:.clusterid.fctr4` 0.997515
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## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
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## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
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## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
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## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
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## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 0.995923
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 0.196053
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` 0.470798
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 0.569605
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 0.995957
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` 0.993865
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 0.604836
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 0.997215
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` 0.703959
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 0.995529
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 0.996928
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` 0.422980
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 0.295248
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 0.118484
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` 0.819367
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 0.183467
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 0.739448
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` 0.215003
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 0.996470
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 0.999729
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 0.399798
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 0.703171
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 0.141783
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 0.750581
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` 0.487404
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr##:.clusterid.fctr18` 0.271211
## `myCategory.fctr#Multimedia#:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr18` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr18` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18` NA
## `myCategory.fctrmyOther:.clusterid.fctr18` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr18` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18` NA
## `myCategory.fctrTStyle##:.clusterid.fctr18` NA
## `myCategory.fctr##:.clusterid.fctr19` 0.521026
## `myCategory.fctr#Multimedia#:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19` NA
## `myCategory.fctrmyOther:.clusterid.fctr19` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr19` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
## `myCategory.fctrTStyle##:.clusterid.fctr19` NA
## `myCategory.fctr##:.clusterid.fctr20` 0.997159
## `myCategory.fctr#Multimedia#:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20` NA
## `myCategory.fctrmyOther:.clusterid.fctr20` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr20` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
##
## (Intercept) **
## WordCount.log ***
## A.ratio.sum.TfIdf.nwrds ***
## H.ratio.sum.TfIdf.nwrds
## `myCategory.fctr#Multimedia#` *
## `myCategory.fctr#Opinion#Room For Debate` ***
## `myCategory.fctr#Opinion#The Public Editor` **
## `myCategory.fctr#U.S.#Education`
## `myCategory.fctrBusiness#Business Day#Dealbook`
## `myCategory.fctrBusiness#Business Day#Small Business` **
## `myCategory.fctrBusiness#Crosswords/Games#` ***
## `myCategory.fctrBusiness#Technology#`
## `myCategory.fctrCulture#Arts#`
## `myCategory.fctrForeign#World#`
## `myCategory.fctrForeign#World#Asia Pacific` *
## `myCategory.fctrMetro#N.Y. / Region#`
## myCategory.fctrmyOther
## `myCategory.fctrOpEd#Opinion#` ***
## `myCategory.fctrScience#Health#` ***
## `myCategory.fctrStyles##Fashion`
## `myCategory.fctrStyles#U.S.#` ***
## `myCategory.fctrTravel#Travel#`
## `myCategory.fctrTStyle##` ***
## PubDate.day.minutes.poly.1
## H.sum.TfIdf
## S.sum.TfIdf *
## H.npnct19.log ***
## A.ratio.nstopwrds.nwrds **
## PubDate.wkend
## H.P.recap.colon
## H.P.quandary
## PubDate.day.minutes.poly.4
## PubDate.day.minutes.poly.2 **
## H.P.no.comment.colon *
## S.npnct19.log ***
## H.P.facts.figures
## H.npnct08.log *
## PubDate.last10.log
## PubDate.last1.log
## H.P.readers.respond ***
## A.T.make .
## H.ratio.nstopwrds.nwrds
## H.T.get
## H.npnct06.log
## A.npnct01.log
## A.T.can
## PubDate.day.minutes.poly.3
## H.T.ebola
## H.npnct01.log
## A.T.said
## H.T.make
## H.npnct11.log *
## A.T.one
## H.P.s.notebook
## H.T.take
## A.npnct16.log
## S.npnct16.log
## A.T.presid
## S.T.presid
## S.npnct08.log
## A.npnct08.log
## PubDate.last100.log
## .rnorm
## H.npnct05.log
## H.P.friday.night.music .
## H.T.say
## H.T.obama
## H.T.bank
## `PubDate.date.fctr(7,13]`
## `PubDate.date.fctr(13,19]`
## `PubDate.date.fctr(19,25]`
## `PubDate.date.fctr(25,31]`
## `PubDate.second.fctr(14.8,29.5]`
## `PubDate.second.fctr(29.5,44.2]`
## `PubDate.second.fctr(44.2,59.1]`
## H.npnct07.log
## A.npnct07.log
## H.npnct12.log
## A.npnct03.log
## H.T.big
## A.P.year.colon
## A.T.obama
## A.npnct20.log
## H.npnct02.log
## H.T.test
## S.npnct14.log
## A.P.first.draft
## H.P.on.this.day
## A.T.take
## A.npnct06.log
## A.npnct14.log
## A.T.time
## H.T.newyorktim
## H.npnct13.log
## H.T.deal
## A.T.new
## A.P.metropolitan.diary.colon
## H.P.verbatim.colon
## H.T.china
## H.T.art
## `PubDate.minute.fctr(14.8,29.5]`
## `PubDate.minute.fctr(29.5,44.2]`
## `PubDate.minute.fctr(44.2,59.1]`
## S.T.year
## A.npnct12.log
## H.P.what.we.are
## S.T.will
## A.T.appear
## PubDate.wkday.fctr1
## PubDate.wkday.fctr2
## PubDate.wkday.fctr3
## PubDate.wkday.fctr4
## PubDate.wkday.fctr5
## PubDate.wkday.fctr6
## H.T.pictur
## H.T.new
## S.T.senat
## A.T.show
## H.P.today.in.smallbusiness
## A.T.day
## H.T.news
## S.T.first
## H.T.first
## H.T.X2014
## S.T.newyork *
## S.T.report
## S.T.compani
## S.T.word
## H.T.morn
## H.T.busi
## S.T.newyorktim
## A.T.share *
## H.npnct04.log *
## S.npnct13.log **
## S.T.articl .
## H.T.newyork
## PubDate.day.minutes.poly.5
## H.T.today .
## H.T.day
## A.npnct04.log
## H.T.daili
## H.T.week
## S.T.photo
## A.npnct15.log
## H.P.year.colon
## H.T.fashion
## H.npnct15.log **
## S.T.fashion
## S.T.week
## H.nstopwrds.log
## H.npnct28.log
## A.npnct11.log
## H.ndgts.log .
## A.ndgts.log .
## H.nwrds.unq.log *
## A.nuppr.log **
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## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` .
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## `myCategory.fctr#U.S.#Education:.clusterid.fctr18`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr18`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr18`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr18`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr18`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr18`
## `myCategory.fctrForeign#World#:.clusterid.fctr18`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18`
## `myCategory.fctrmyOther:.clusterid.fctr18`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18`
## `myCategory.fctrScience#Health#:.clusterid.fctr18`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18`
## `myCategory.fctrTStyle##:.clusterid.fctr18`
## `myCategory.fctr##:.clusterid.fctr19`
## `myCategory.fctr#Multimedia#:.clusterid.fctr19`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19`
## `myCategory.fctrForeign#World#:.clusterid.fctr19`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19`
## `myCategory.fctrmyOther:.clusterid.fctr19`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19`
## `myCategory.fctrScience#Health#:.clusterid.fctr19`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19`
## `myCategory.fctrTStyle##:.clusterid.fctr19`
## `myCategory.fctr##:.clusterid.fctr20`
## `myCategory.fctr#Multimedia#:.clusterid.fctr20`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20`
## `myCategory.fctrForeign#World#:.clusterid.fctr20`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20`
## `myCategory.fctrmyOther:.clusterid.fctr20`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20`
## `myCategory.fctrScience#Health#:.clusterid.fctr20`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20`
## `myCategory.fctrTStyle##:.clusterid.fctr20`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 1659.4 on 4254 degrees of freedom
## AIC: 2101.4
##
## Number of Fisher Scoring iterations: 19
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.6784520
## 3 0.2 0.7494382
## 4 0.3 0.7662740
## 5 0.4 0.7575150
## 6 0.5 0.7435530
## 7 0.6 0.7332832
## 8 0.7 0.6866397
## 9 0.8 0.6039076
## 10 0.9 0.4742063
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Low.cor.X.glm.N
## 1 N 3480
## 2 Y 131
## Popular.fctr.predict.Low.cor.X.glm.Y
## 1 246
## 2 618
## Prediction
## Reference N Y
## N 3480 246
## Y 131 618
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.157542e-01 7.152089e-01 9.072305e-01 9.237306e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 4.253889e-59 4.323944e-09
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.6511628
## 3 0.2 0.6912669
## 4 0.3 0.7061995
## 5 0.4 0.7230321
## 6 0.5 0.7047913
## 7 0.6 0.6820428
## 8 0.7 0.6445993
## 9 0.8 0.5763359
## 10 0.9 0.4575163
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Low.cor.X.glm.N
## 1 N 1619
## 2 Y 96
## Popular.fctr.predict.Low.cor.X.glm.Y
## 1 94
## 2 248
## Prediction
## Reference N Y
## N 1619 94
## Y 96 248
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.076325e-01 6.676067e-01 8.942890e-01 9.198004e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 9.289451e-23 9.421661e-01
## model_id model_method
## 1 Low.cor.X.glm glm
## feats
## 1 WordCount.log, A.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, H.npnct19.log, A.ratio.nstopwrds.nwrds, PubDate.wkend, H.P.recap.colon, H.P.quandary, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, H.P.no.comment.colon, S.npnct19.log, H.P.facts.figures, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.P.readers.respond, A.T.make, H.ratio.nstopwrds.nwrds, H.T.get, H.npnct06.log, A.npnct01.log, A.T.can, PubDate.day.minutes.poly.3, H.T.ebola, H.npnct01.log, A.T.said, H.T.make, H.npnct11.log, A.T.one, H.P.s.notebook, H.T.take, A.npnct16.log, S.npnct16.log, A.T.presid, S.T.presid, S.npnct08.log, A.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, H.P.friday.night.music, H.T.say, H.T.obama, H.T.bank, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, H.npnct12.log, A.npnct03.log, H.T.big, A.P.year.colon, A.T.obama, A.npnct20.log, H.npnct02.log, H.T.test, S.npnct14.log, A.P.first.draft, H.P.on.this.day, A.T.take, A.npnct06.log, A.npnct14.log, A.T.time, H.T.newyorktim, H.npnct13.log, H.T.deal, A.T.new, A.P.metropolitan.diary.colon, H.P.verbatim.colon, H.T.china, H.T.art, PubDate.minute.fctr, S.T.year, A.npnct12.log, H.P.what.we.are, S.T.will, A.T.appear, PubDate.wkday.fctr, H.T.pictur, H.T.new, S.T.senat, A.T.show, H.P.today.in.smallbusiness, A.T.day, H.T.news, S.T.first, H.T.first, H.T.X2014, S.T.newyork, S.T.report, S.T.compani, S.T.word, H.T.morn, H.T.busi, S.T.newyorktim, A.T.share, H.npnct04.log, S.npnct13.log, S.T.articl, H.T.newyork, PubDate.day.minutes.poly.5, H.T.today, H.T.day, A.npnct04.log, H.T.daili, H.T.week, S.T.photo, A.npnct15.log, H.P.year.colon, H.T.fashion, H.npnct15.log, S.T.fashion, S.T.week, H.nstopwrds.log, H.npnct28.log, A.npnct11.log, H.ndgts.log, A.ndgts.log, H.nwrds.unq.log, A.nuppr.log, myCategory.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 147.979 68.071
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9609008 0.3 0.766274 0.90324
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9072305 0.9237306 0.6385029 0.9169373
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7230321 0.9076325
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.894289 0.9198004 0.6676067 2101.413
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.005126348 0.01436941rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 10 fit.models 7 0 420.866 622.161 201.295
## 11 fit.models 7 1 622.162 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn")## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 630.801 NA NA# Options:
# 1. rpart & rf manual tuning
# 2. rf without pca (default: with pca)
#stop(here); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
#glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
# All X that is not user excluded
# if (glb_is_classification && glb_is_binomial) {
# model_id_pfx <- "Conditional.X"
# # indep_vars_vctr <- setdiff(names(glb_fitobs_df), union(glb_rsp_var, glb_exclude_vars_as_features))
# indep_vars_vctr <- subset(glb_feats_df, is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"]
# } else {
model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df, !myNearZV &
(exclude.as.feat != 1))[, "id"]
# }
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
for (method in glb_models_method_vctr) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", method), major.inc=TRUE)
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars_vctr <- setdiff(indep_vars_vctr, c(".rnorm"))
model_id <- paste0(model_id_pfx, ".no.rnorm")
} else model_id <- model_id_pfx
if (method %in% c("glm")) # for a "robust" glm model
indep_vars_vctr <- setdiff(indep_vars_vctr, c(NULL
,"A.nchrs.log" # correlated to "S.*"
,"A.ndgts.log" # correlated to "S.*"
,"A.nuppr.log" # correlated to "S.*"
,"A.npnct01.log" # identical to "S.npnct01.log"
,"A.npnct03.log" # correlated to "S.npnct03.log"
,"A.npnct04.log" # correlated to "S.npnct04.log"
,"A.npnct06.log" # identical to "S.npnct06.log"
,"A.npnct07.log" # identical to "S.npnct07.log"
,"A.npnct08.log" # correlated to "S.npnct08.log"
,"A.npnct11.log" # correlated to "S.*"
,"A.npnct12.log" # correlated to "S.*"
,"S.npnct14.log" # correlated to "A.*"
,"A.npnct15.log" # correlated to "S.npnct15.log"
,"A.npnct16.log" # correlated to "S.npnct16.log"
,"A.npnct19.log" # correlated to "S.*"
,"A.npnct20.log" # identical to "S.npnct20.log"
,"A.npnct21.log" # correlated to "S.npnct21.log"
,"A.P.daily.clip.report" # identical to "S.*"
,"S.P.daily.clip.report" # identical to "H.*"
,"A.P.http" # correlated to "A.npnct14.log"
,"A.P.fashion.week" # identical to "S.*"
,"H.P.first.draft" # correlated to "H.T.first"
,"A.P.first.draft" # identical to "S.*"
,"A.P.metropolitan.diary.colon" # identical to "S.*"
,"A.P.year.colon" # identical to "S.P.year.colon"
))
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# If All.X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(model_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.nuppr.log, glb_allobs_df$A.nuppr.log)
# all.equal(glb_allobs_df$S.npnct19.log, glb_allobs_df$A.npnct19.log)
# all.equal(glb_allobs_df$S.P.year.colon, glb_allobs_df$A.P.year.colon)
# all.equal(glb_allobs_df$S.T.share, glb_allobs_df$A.T.share)
# all.equal(glb_allobs_df$H.T.clip, glb_allobs_df$H.P.daily.clip.report)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# dsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# dsp_obs(Abstract.contains="[Ss]hare", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
}## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 630.801 630.817 0.016
## 2 fit.models_1_glm 2 0 630.818 NA NA
## [1] "fitting model: All.X.glm"
## [1] " indep_vars: WordCount.log, A.ratio.sum.TfIdf.nwrds, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, H.P.recap.colon, H.P.quandary, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, H.P.no.comment.colon, S.npnct19.log, H.P.facts.figures, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.P.readers.respond, A.T.make, S.T.make, H.ratio.nstopwrds.nwrds, H.T.get, H.npnct06.log, S.npnct01.log, A.T.can, H.npnct16.log, S.T.can, PubDate.day.minutes.poly.3, H.T.ebola, H.npnct01.log, A.T.said, S.T.said, H.T.make, H.npnct11.log, A.T.one, S.T.one, H.P.s.notebook, H.T.take, S.npnct16.log, A.T.presid, S.T.presid, S.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, H.P.friday.night.music, H.T.say, H.T.obama, H.T.bank, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, H.T.word, H.T.big, A.npnct02.log, A.npnct17.log, S.P.year.colon, S.T.obama, A.T.obama, S.npnct20.log, H.npnct02.log, H.T.test, H.P.on.this.day, S.P.first.draft, S.T.take, A.T.take, S.npnct06.log, A.npnct14.log, S.T.time, A.T.time, H.T.newyorktim, H.npnct13.log, H.T.deal, S.T.new, A.T.new, H.T.billion, S.P.metropolitan.diary.colon, H.T.polit, H.P.verbatim.colon, H.T.china, H.T.art, PubDate.minute.fctr, H.T.read, S.npnct12.log, H.P.today.in.politic, A.T.year, S.T.year, H.P.what.we.are, A.T.will, S.T.will, A.T.appear, S.T.appear, PubDate.wkday.fctr, H.T.pictur, H.T.new, A.T.senat, S.T.senat, S.T.show, A.T.show, H.P.today.in.smallbusiness, S.T.day, A.T.day, S.npnct28.log, A.npnct28.log, H.P.daily.clip.report, H.T.clip, A.T.first, H.T.news, S.T.first, H.T.first, H.T.X2014, A.T.newyork, S.T.newyork, A.T.report, A.T.compani, S.T.report, S.T.compani, A.T.word, S.T.word, H.T.morn, H.T.busi, A.T.newyorktim, S.T.newyorktim, A.npnct13.log, A.T.share, S.T.share, H.npnct04.log, S.npnct13.log, A.T.articl, S.T.articl, H.T.newyork, PubDate.day.minutes.poly.5, H.T.today, H.T.springsumm, H.T.day, H.npnct14.log, A.T.diari, S.T.diari, H.T.report, S.npnct04.log, H.T.daili, H.T.X2015, A.T.herald, S.T.herald, S.npnct15.log, H.T.week, A.T.photo, S.T.photo, A.T.intern, S.T.intern, A.T.tribun, S.T.tribun, S.P.fashion.week, A.T.archiv, S.T.archiv, H.P.fashion.week, H.P.year.colon, H.T.fashion, H.npnct15.log, A.T.fashion, S.T.fashion, A.T.week, S.T.week, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: not plotting observations with leverage one:
## 129, 1163, 3511, 3846, 3953
## Warning: not plotting observations with leverage one:
## 129, 1163, 3511, 3846, 3953
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.9972 -0.2543 -0.0601 0.0000 3.4198
##
## Coefficients: (321 not defined because of singularities)
## Estimate
## (Intercept) -8.629e+00
## WordCount.log 1.277e+00
## A.ratio.sum.TfIdf.nwrds -2.035e+02
## S.ratio.sum.TfIdf.nwrds 2.051e+02
## H.ratio.sum.TfIdf.nwrds 3.078e-01
## `myCategory.fctr#Multimedia#` -1.771e+00
## `myCategory.fctr#Opinion#Room For Debate` -4.958e+00
## `myCategory.fctr#Opinion#The Public Editor` 5.093e+00
## `myCategory.fctr#U.S.#Education` -5.577e+01
## `myCategory.fctrBusiness#Business Day#Dealbook` -4.562e-01
## `myCategory.fctrBusiness#Business Day#Small Business` -1.875e+00
## `myCategory.fctrBusiness#Crosswords/Games#` 3.311e+00
## `myCategory.fctrBusiness#Technology#` 1.333e-01
## `myCategory.fctrCulture#Arts#` -2.342e-01
## `myCategory.fctrForeign#World#` -1.825e+01
## `myCategory.fctrForeign#World#Asia Pacific` -1.479e+00
## `myCategory.fctrMetro#N.Y. / Region#` 1.008e+00
## myCategory.fctrmyOther -2.017e+01
## `myCategory.fctrOpEd#Opinion#` 3.367e+00
## `myCategory.fctrScience#Health#` 2.832e+00
## `myCategory.fctrStyles##Fashion` -2.344e+01
## `myCategory.fctrStyles#U.S.#` 1.784e+00
## `myCategory.fctrTravel#Travel#` -1.324e+00
## `myCategory.fctrTStyle##` -1.982e+00
## PubDate.day.minutes.poly.1 1.101e+01
## H.sum.TfIdf -9.231e-02
## S.sum.TfIdf -2.139e+01
## A.sum.TfIdf 2.112e+01
## `PubDate.hour.fctr(7.67,15.3]` 8.165e-01
## `PubDate.hour.fctr(15.3,23]` 8.820e-02
## H.npnct19.log 1.431e+00
## A.ratio.nstopwrds.nwrds 3.648e+04
## S.ratio.nstopwrds.nwrds -3.648e+04
## PubDate.wkend -3.531e-01
## H.P.recap.colon -2.236e+01
## H.P.quandary 2.513e+01
## PubDate.day.minutes.poly.4 -1.624e+01
## PubDate.day.minutes.poly.2 4.531e+01
## H.P.no.comment.colon 2.985e+00
## S.npnct19.log 1.693e+00
## H.P.facts.figures 7.936e-01
## H.npnct08.log 1.082e+00
## PubDate.last10.log 9.865e-02
## PubDate.last1.log -6.634e-02
## H.P.readers.respond 7.422e+00
## A.T.make -1.546e+04
## S.T.make 1.544e+04
## H.ratio.nstopwrds.nwrds 5.327e+00
## H.T.get 4.850e-01
## H.npnct06.log 1.346e+00
## S.npnct01.log 9.784e-01
## A.T.can 1.412e+03
## H.npnct16.log -5.370e-01
## S.T.can -1.414e+03
## PubDate.day.minutes.poly.3 8.166e+00
## H.T.ebola -3.845e-01
## H.npnct01.log -1.518e+00
## A.T.said -9.323e+03
## S.T.said 9.323e+03
## H.T.make -1.027e-01
## H.npnct11.log 4.016e-01
## A.T.one 2.232e+04
## S.T.one -2.230e+04
## H.P.s.notebook -1.868e+01
## H.T.take -1.336e-02
## S.npnct16.log -2.349e-01
## A.T.presid 5.854e+03
## S.T.presid -5.853e+03
## S.npnct08.log 8.918e-01
## PubDate.last100.log 2.037e-02
## .rnorm -9.056e-02
## H.npnct05.log -2.843e+01
## H.P.friday.night.music -2.491e+00
## H.T.say -8.414e-01
## H.T.obama -2.452e-02
## H.T.bank 1.483e+00
## `PubDate.date.fctr(7,13]` 4.311e-02
## `PubDate.date.fctr(13,19]` -1.057e-01
## `PubDate.date.fctr(19,25]` -9.545e-02
## `PubDate.date.fctr(25,31]` 1.732e-01
## `PubDate.second.fctr(14.8,29.5]` 1.020e-01
## `PubDate.second.fctr(29.5,44.2]` -2.061e-03
## `PubDate.second.fctr(44.2,59.1]` -2.961e-01
## H.npnct07.log 2.776e-01
## S.npnct07.log -3.395e+01
## S.npnct03.log -3.427e+01
## A.npnct18.log 5.720e+01
## H.npnct12.log 3.916e-01
## H.T.word 1.879e+00
## H.T.big -8.129e-01
## A.npnct02.log -1.978e+01
## A.npnct17.log 5.217e+02
## S.P.year.colon 6.197e+00
## S.T.obama -4.886e+03
## A.T.obama 4.887e+03
## S.npnct20.log -2.953e+01
## H.npnct02.log -2.040e+01
## H.T.test 2.051e-01
## H.P.on.this.day -1.715e+01
## S.P.first.draft -1.753e+01
## S.T.take 2.105e+03
## A.T.take -2.108e+03
## S.npnct06.log -5.624e-01
## A.npnct14.log 7.674e-01
## S.T.time 4.018e+04
## A.T.time -4.023e+04
## H.T.newyorktim -5.982e-01
## H.npnct13.log -2.362e-01
## H.T.deal -2.590e+01
## S.T.new -1.086e+04
## A.T.new 1.086e+04
## H.T.billion 7.343e-01
## S.P.metropolitan.diary.colon -9.571e+00
## H.T.polit 6.078e-01
## H.P.verbatim.colon -1.694e+01
## H.T.china -1.166e+00
## H.T.art -1.530e+00
## `PubDate.minute.fctr(14.8,29.5]` -6.721e-02
## `PubDate.minute.fctr(29.5,44.2]` -1.541e-01
## `PubDate.minute.fctr(44.2,59.1]` 5.815e-02
## H.T.read -6.563e-01
## S.npnct12.log -1.812e-01
## H.P.today.in.politic -1.964e+01
## A.T.year 1.922e+03
## S.T.year -1.921e+03
## H.P.what.we.are -2.061e+01
## A.T.will 3.718e+03
## S.T.will -3.715e+03
## A.T.appear -1.488e-01
## S.T.appear NA
## PubDate.wkday.fctr1 -7.268e-01
## PubDate.wkday.fctr2 -1.270e+00
## PubDate.wkday.fctr3 -7.817e-01
## PubDate.wkday.fctr4 -1.108e+00
## PubDate.wkday.fctr5 -8.261e-01
## PubDate.wkday.fctr6 -1.056e+00
## H.T.pictur 1.473e-02
## H.T.new -1.882e-01
## A.T.senat 1.859e+03
## S.T.senat -1.855e+03
## S.T.show 1.024e+04
## A.T.show -1.024e+04
## H.P.today.in.smallbusiness -1.693e+01
## S.T.day -2.562e+03
## A.T.day 2.567e+03
## S.npnct28.log 6.546e+02
## A.npnct28.log -6.872e+02
## H.P.daily.clip.report 4.619e+01
## H.T.clip NA
## A.T.first 5.648e+03
## H.T.news -9.072e-01
## S.T.first -5.646e+03
## H.T.first -3.086e-01
## H.T.X2014 -7.860e-01
## A.T.newyork 4.180e+04
## S.T.newyork -4.180e+04
## A.T.report -2.760e+04
## A.T.compani 5.509e+03
## S.T.report 2.760e+04
## S.T.compani -5.510e+03
## A.T.word -6.188e+03
## S.T.word 6.188e+03
## H.T.morn 1.624e+01
## H.T.busi -5.762e-01
## A.T.newyorktim -7.427e+03
## S.T.newyorktim 7.416e+03
## A.npnct13.log 1.198e+02
## A.T.share -2.673e+00
## S.T.share NA
## H.npnct04.log -2.901e+00
## S.npnct13.log -1.189e+02
## A.T.articl -1.349e+04
## S.T.articl 1.349e+04
## H.T.newyork -6.313e-01
## PubDate.day.minutes.poly.5 -8.073e+00
## H.T.today -7.130e-01
## H.T.springsumm 3.305e+01
## H.T.day -9.432e-01
## H.npnct14.log -2.489e+01
## A.T.diari 2.037e+01
## S.T.diari NA
## H.T.report -6.911e-01
## S.npnct04.log -1.343e+00
## H.T.daili -2.978e+01
## H.T.X2015 -3.096e+01
## A.T.herald 7.430e+01
## S.T.herald NA
## S.npnct15.log 2.578e-01
## H.T.week -6.903e-01
## A.T.photo -2.928e+00
## S.T.photo NA
## A.T.intern 5.882e+02
## S.T.intern -5.909e+02
## A.T.tribun -5.145e+01
## S.T.tribun NA
## S.P.fashion.week 6.193e-01
## A.T.archiv -5.323e+01
## S.T.archiv NA
## H.P.fashion.week -1.676e+01
## H.P.year.colon -1.792e+01
## H.T.fashion 2.073e+00
## H.npnct15.log -1.226e+00
## A.T.fashion -4.471e+04
## S.T.fashion 4.465e+04
## A.T.week 5.230e+02
## S.T.week -5.227e+02
## H.nstopwrds.log -1.185e+00
## H.npnct28.log -1.813e+00
## S.npnct11.log -2.345e-01
## S.nstopwrds.log 1.039e+04
## A.nstopwrds.log -1.038e+04
## H.ndgts.log 7.754e-01
## S.ndgts.log -3.104e-01
## H.nuppr.log 1.945e+00
## H.nwrds.log 8.537e-01
## H.nchrs.log -3.369e-01
## S.nwrds.log -7.397e+03
## A.nwrds.log 7.397e+03
## H.nwrds.unq.log -1.286e+00
## S.nchrs.log -1.677e-01
## A.nwrds.unq.log 3.780e+03
## S.nwrds.unq.log -3.781e+03
## S.nuppr.log -5.373e-01
## `myCategory.fctr##:.clusterid.fctr2` -2.828e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 4.562e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 2.532e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 5.727e-02
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 4.043e-03
## `myCategory.fctrScience#Health#:.clusterid.fctr2` -2.860e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 1.492e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` -7.882e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` -1.271e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 9.858e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` -1.978e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 2.311e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr3` -3.007e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` -1.205e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` -1.172e+01
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 2.013e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 6.514e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 2.268e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 5.483e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` -5.267e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` -1.107e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` -4.589e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 5.774e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` -3.174e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 7.430e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` -6.132e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` -3.651e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` -2.755e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` -1.949e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` -1.952e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` -1.014e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` -5.350e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 2.178e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 8.226e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 4.484e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` -2.050e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 1.464e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` -3.204e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` -7.348e-02
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` -3.664e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` -1.079e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` -4.343e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 4.552e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` -1.871e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` -1.856e+01
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 4.706e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` -1.944e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` -2.218e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` -2.009e+01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` -1.891e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` -9.354e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` -1.239e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 2.036e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` 2.642e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` -2.055e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 3.619e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` -1.840e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` -8.310e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` -7.446e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 3.991e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 5.032e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` -4.468e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 1.321e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` 7.901e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr##:.clusterid.fctr18` -1.416e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr18` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr18` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18` NA
## `myCategory.fctrmyOther:.clusterid.fctr18` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr18` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18` NA
## `myCategory.fctrTStyle##:.clusterid.fctr18` NA
## `myCategory.fctr##:.clusterid.fctr19` -1.192e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19` NA
## `myCategory.fctrmyOther:.clusterid.fctr19` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr19` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
## `myCategory.fctrTStyle##:.clusterid.fctr19` NA
## `myCategory.fctr##:.clusterid.fctr20` -2.154e+01
## `myCategory.fctr#Multimedia#:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20` NA
## `myCategory.fctrmyOther:.clusterid.fctr20` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr20` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
## Std. Error
## (Intercept) 5.224e+00
## WordCount.log 1.114e-01
## A.ratio.sum.TfIdf.nwrds 2.020e+02
## S.ratio.sum.TfIdf.nwrds 2.019e+02
## H.ratio.sum.TfIdf.nwrds 2.263e-01
## `myCategory.fctr#Multimedia#` 8.689e-01
## `myCategory.fctr#Opinion#Room For Debate` 8.731e-01
## `myCategory.fctr#Opinion#The Public Editor` 1.641e+00
## `myCategory.fctr#U.S.#Education` 4.393e+03
## `myCategory.fctrBusiness#Business Day#Dealbook` 4.284e-01
## `myCategory.fctrBusiness#Business Day#Small Business` 7.483e-01
## `myCategory.fctrBusiness#Crosswords/Games#` 6.091e-01
## `myCategory.fctrBusiness#Technology#` 8.090e-01
## `myCategory.fctrCulture#Arts#` 6.176e-01
## `myCategory.fctrForeign#World#` 4.788e+03
## `myCategory.fctrForeign#World#Asia Pacific` 7.167e-01
## `myCategory.fctrMetro#N.Y. / Region#` 6.453e-01
## myCategory.fctrmyOther 7.996e+03
## `myCategory.fctrOpEd#Opinion#` 3.943e-01
## `myCategory.fctrScience#Health#` 4.783e-01
## `myCategory.fctrStyles##Fashion` 4.477e+03
## `myCategory.fctrStyles#U.S.#` 5.025e-01
## `myCategory.fctrTravel#Travel#` 1.095e+00
## `myCategory.fctrTStyle##` 5.844e-01
## PubDate.day.minutes.poly.1 1.783e+01
## H.sum.TfIdf 9.288e-02
## S.sum.TfIdf 1.613e+01
## A.sum.TfIdf 1.614e+01
## `PubDate.hour.fctr(7.67,15.3]` 5.384e-01
## `PubDate.hour.fctr(15.3,23]` 6.129e-01
## H.npnct19.log 3.669e-01
## A.ratio.nstopwrds.nwrds 4.701e+06
## S.ratio.nstopwrds.nwrds 4.701e+06
## PubDate.wkend 5.147e-01
## H.P.recap.colon 2.383e+04
## H.P.quandary 2.007e+04
## PubDate.day.minutes.poly.4 1.109e+01
## PubDate.day.minutes.poly.2 1.368e+01
## H.P.no.comment.colon 1.261e+00
## S.npnct19.log 3.839e-01
## H.P.facts.figures 1.652e+00
## H.npnct08.log 5.037e-01
## PubDate.last10.log 1.474e-01
## PubDate.last1.log 4.862e-02
## H.P.readers.respond 1.144e+00
## A.T.make 4.057e+06
## S.T.make 4.053e+06
## H.ratio.nstopwrds.nwrds 2.903e+00
## H.T.get 4.392e-01
## H.npnct06.log 1.257e+00
## S.npnct01.log 2.063e+00
## A.T.can 1.653e+05
## H.npnct16.log 1.263e+00
## S.T.can 1.653e+05
## PubDate.day.minutes.poly.3 7.320e+00
## H.T.ebola 4.412e-01
## H.npnct01.log 1.415e+00
## A.T.said 9.653e+05
## S.T.said 9.653e+05
## H.T.make 4.339e-01
## H.npnct11.log 2.329e-01
## A.T.one 1.645e+06
## S.T.one 1.643e+06
## H.P.s.notebook 2.365e+04
## H.T.take 5.473e-01
## S.npnct16.log 1.427e+00
## A.T.presid 4.526e+05
## S.T.presid 4.526e+05
## S.npnct08.log 6.758e-01
## PubDate.last100.log 4.849e-02
## .rnorm 6.965e-02
## H.npnct05.log 2.789e+04
## H.P.friday.night.music 1.396e+00
## H.T.say 5.189e-01
## H.T.obama 6.982e-01
## H.T.bank 8.896e-01
## `PubDate.date.fctr(7,13]` 2.171e-01
## `PubDate.date.fctr(13,19]` 2.160e-01
## `PubDate.date.fctr(19,25]` 2.102e-01
## `PubDate.date.fctr(25,31]` 2.269e-01
## `PubDate.second.fctr(14.8,29.5]` 1.936e-01
## `PubDate.second.fctr(29.5,44.2]` 1.901e-01
## `PubDate.second.fctr(44.2,59.1]` 1.969e-01
## H.npnct07.log 2.327e-01
## S.npnct07.log 2.662e+04
## S.npnct03.log 2.346e+04
## A.npnct18.log 1.220e+05
## H.npnct12.log 3.545e-01
## H.T.word 9.295e-01
## H.T.big 7.477e-01
## A.npnct02.log 4.162e+04
## A.npnct17.log 5.531e+04
## S.P.year.colon 1.157e+04
## S.T.obama 3.261e+05
## A.T.obama 3.261e+05
## S.npnct20.log 1.937e+04
## H.npnct02.log 1.359e+04
## H.T.test 9.645e-01
## H.P.on.this.day 1.515e+04
## S.P.first.draft 1.175e+04
## S.T.take 1.132e+07
## A.T.take 1.133e+07
## S.npnct06.log 1.862e+00
## A.npnct14.log 1.978e+00
## S.T.time 4.250e+06
## A.T.time 4.254e+06
## H.T.newyorktim 8.254e-01
## H.npnct13.log 2.214e-01
## H.T.deal 6.597e+03
## S.T.new 3.171e+06
## A.T.new 3.173e+06
## H.T.billion 1.842e+00
## S.P.metropolitan.diary.colon 4.029e+00
## H.T.polit 8.577e-01
## H.P.verbatim.colon 9.728e+03
## H.T.china 1.090e+00
## H.T.art 1.010e+00
## `PubDate.minute.fctr(14.8,29.5]` 2.019e-01
## `PubDate.minute.fctr(29.5,44.2]` 1.973e-01
## `PubDate.minute.fctr(44.2,59.1]` 2.048e-01
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## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 1.285e+04
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 1.304e+04
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 7.029e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 1.055e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 1.724e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 1.294e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` 9.317e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr##:.clusterid.fctr18` 1.476e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr18` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr18` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18` NA
## `myCategory.fctrmyOther:.clusterid.fctr18` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr18` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18` NA
## `myCategory.fctrTStyle##:.clusterid.fctr18` NA
## `myCategory.fctr##:.clusterid.fctr19` 1.788e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19` NA
## `myCategory.fctrmyOther:.clusterid.fctr19` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr19` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
## `myCategory.fctrTStyle##:.clusterid.fctr19` NA
## `myCategory.fctr##:.clusterid.fctr20` 1.388e+04
## `myCategory.fctr#Multimedia#:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20` NA
## `myCategory.fctrmyOther:.clusterid.fctr20` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr20` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
## z value
## (Intercept) -1.652
## WordCount.log 11.467
## A.ratio.sum.TfIdf.nwrds -1.007
## S.ratio.sum.TfIdf.nwrds 1.016
## H.ratio.sum.TfIdf.nwrds 1.360
## `myCategory.fctr#Multimedia#` -2.039
## `myCategory.fctr#Opinion#Room For Debate` -5.679
## `myCategory.fctr#Opinion#The Public Editor` 3.104
## `myCategory.fctr#U.S.#Education` -0.013
## `myCategory.fctrBusiness#Business Day#Dealbook` -1.065
## `myCategory.fctrBusiness#Business Day#Small Business` -2.506
## `myCategory.fctrBusiness#Crosswords/Games#` 5.435
## `myCategory.fctrBusiness#Technology#` 0.165
## `myCategory.fctrCulture#Arts#` -0.379
## `myCategory.fctrForeign#World#` -0.004
## `myCategory.fctrForeign#World#Asia Pacific` -2.064
## `myCategory.fctrMetro#N.Y. / Region#` 1.563
## myCategory.fctrmyOther -0.003
## `myCategory.fctrOpEd#Opinion#` 8.538
## `myCategory.fctrScience#Health#` 5.922
## `myCategory.fctrStyles##Fashion` -0.005
## `myCategory.fctrStyles#U.S.#` 3.550
## `myCategory.fctrTravel#Travel#` -1.209
## `myCategory.fctrTStyle##` -3.391
## PubDate.day.minutes.poly.1 0.618
## H.sum.TfIdf -0.994
## S.sum.TfIdf -1.326
## A.sum.TfIdf 1.308
## `PubDate.hour.fctr(7.67,15.3]` 1.517
## `PubDate.hour.fctr(15.3,23]` 0.144
## H.npnct19.log 3.899
## A.ratio.nstopwrds.nwrds 0.008
## S.ratio.nstopwrds.nwrds -0.008
## PubDate.wkend -0.686
## H.P.recap.colon -0.001
## H.P.quandary 0.001
## PubDate.day.minutes.poly.4 -1.464
## PubDate.day.minutes.poly.2 3.312
## H.P.no.comment.colon 2.367
## S.npnct19.log 4.410
## H.P.facts.figures 0.480
## H.npnct08.log 2.149
## PubDate.last10.log 0.670
## PubDate.last1.log -1.364
## H.P.readers.respond 6.486
## A.T.make -0.004
## S.T.make 0.004
## H.ratio.nstopwrds.nwrds 1.835
## H.T.get 1.104
## H.npnct06.log 1.071
## S.npnct01.log 0.474
## A.T.can 0.009
## H.npnct16.log -0.425
## S.T.can -0.009
## PubDate.day.minutes.poly.3 1.116
## H.T.ebola -0.871
## H.npnct01.log -1.073
## A.T.said -0.010
## S.T.said 0.010
## H.T.make -0.237
## H.npnct11.log 1.724
## A.T.one 0.014
## S.T.one -0.014
## H.P.s.notebook -0.001
## H.T.take -0.024
## S.npnct16.log -0.165
## A.T.presid 0.013
## S.T.presid -0.013
## S.npnct08.log 1.319
## PubDate.last100.log 0.420
## .rnorm -1.300
## H.npnct05.log -0.001
## H.P.friday.night.music -1.785
## H.T.say -1.622
## H.T.obama -0.035
## H.T.bank 1.667
## `PubDate.date.fctr(7,13]` 0.199
## `PubDate.date.fctr(13,19]` -0.489
## `PubDate.date.fctr(19,25]` -0.454
## `PubDate.date.fctr(25,31]` 0.763
## `PubDate.second.fctr(14.8,29.5]` 0.527
## `PubDate.second.fctr(29.5,44.2]` -0.011
## `PubDate.second.fctr(44.2,59.1]` -1.504
## H.npnct07.log 1.193
## S.npnct07.log -0.001
## S.npnct03.log -0.001
## A.npnct18.log 0.000
## H.npnct12.log 1.105
## H.T.word 2.022
## H.T.big -1.087
## A.npnct02.log 0.000
## A.npnct17.log 0.009
## S.P.year.colon 0.001
## S.T.obama -0.015
## A.T.obama 0.015
## S.npnct20.log -0.002
## H.npnct02.log -0.002
## H.T.test 0.213
## H.P.on.this.day -0.001
## S.P.first.draft -0.001
## S.T.take 0.000
## A.T.take 0.000
## S.npnct06.log -0.302
## A.npnct14.log 0.388
## S.T.time 0.009
## A.T.time -0.009
## H.T.newyorktim -0.725
## H.npnct13.log -1.067
## H.T.deal -0.004
## S.T.new -0.003
## A.T.new 0.003
## H.T.billion 0.399
## S.P.metropolitan.diary.colon -2.376
## H.T.polit 0.709
## H.P.verbatim.colon -0.002
## H.T.china -1.069
## H.T.art -1.514
## `PubDate.minute.fctr(14.8,29.5]` -0.333
## `PubDate.minute.fctr(29.5,44.2]` -0.781
## `PubDate.minute.fctr(44.2,59.1]` 0.284
## H.T.read -0.797
## S.npnct12.log -0.764
## H.P.today.in.politic -0.002
## A.T.year 0.001
## S.T.year -0.001
## H.P.what.we.are -0.003
## A.T.will 0.016
## S.T.will -0.016
## A.T.appear -0.112
## S.T.appear NA
## PubDate.wkday.fctr1 -1.202
## PubDate.wkday.fctr2 -1.923
## PubDate.wkday.fctr3 -1.206
## PubDate.wkday.fctr4 -1.724
## PubDate.wkday.fctr5 -1.273
## PubDate.wkday.fctr6 -1.860
## H.T.pictur 0.022
## H.T.new -0.318
## A.T.senat 0.009
## S.T.senat -0.009
## S.T.show 0.002
## A.T.show -0.002
## H.P.today.in.smallbusiness -0.002
## S.T.day 0.000
## A.T.day 0.000
## S.npnct28.log 0.003
## A.npnct28.log -0.004
## H.P.daily.clip.report 0.001
## H.T.clip NA
## A.T.first 0.002
## H.T.news -1.108
## S.T.first -0.002
## H.T.first -0.257
## H.T.X2014 -0.669
## A.T.newyork 0.011
## S.T.newyork -0.011
## A.T.report -0.012
## A.T.compani 0.007
## S.T.report 0.012
## S.T.compani -0.007
## A.T.word -0.001
## S.T.word 0.001
## H.T.morn 0.001
## H.T.busi -0.714
## A.T.newyorktim -0.001
## S.T.newyorktim 0.001
## A.npnct13.log 0.011
## A.T.share -2.040
## S.T.share NA
## H.npnct04.log -1.809
## S.npnct13.log -0.011
## A.T.articl -0.003
## S.T.articl 0.003
## H.T.newyork -1.013
## PubDate.day.minutes.poly.5 -1.065
## H.T.today -0.977
## H.T.springsumm 0.002
## H.T.day -1.334
## H.npnct14.log -0.002
## A.T.diari 2.396
## S.T.diari NA
## H.T.report -0.642
## S.npnct04.log -1.849
## H.T.daili -0.004
## H.T.X2015 -0.003
## A.T.herald 0.007
## S.T.herald NA
## S.npnct15.log 0.431
## H.T.week -0.983
## A.T.photo -1.248
## S.T.photo NA
## A.T.intern 0.001
## S.T.intern -0.001
## A.T.tribun -0.004
## S.T.tribun NA
## S.P.fashion.week 0.000
## A.T.archiv -0.005
## S.T.archiv NA
## H.P.fashion.week -0.007
## H.P.year.colon -0.004
## H.T.fashion 1.155
## H.npnct15.log -3.128
## A.T.fashion -0.006
## S.T.fashion 0.006
## A.T.week 0.000
## S.T.week 0.000
## H.nstopwrds.log -1.813
## H.npnct28.log -0.815
## S.npnct11.log -1.453
## S.nstopwrds.log 0.006
## A.nstopwrds.log -0.006
## H.ndgts.log 2.456
## S.ndgts.log -1.758
## H.nuppr.log 2.558
## H.nwrds.log 0.755
## H.nchrs.log -0.563
## S.nwrds.log -0.005
## A.nwrds.log 0.005
## H.nwrds.unq.log -2.465
## S.nchrs.log -0.189
## A.nwrds.unq.log 0.018
## S.nwrds.unq.log -0.018
## S.nuppr.log -3.056
## `myCategory.fctr##:.clusterid.fctr2` -1.488
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.741
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 0.290
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 0.058
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 0.007
## `myCategory.fctrScience#Health#:.clusterid.fctr2` -0.442
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 0.245
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` -1.093
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` -1.467
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 1.128
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` -0.003
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 0.364
## `myCategory.fctrScience#Health#:.clusterid.fctr3` -0.461
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` -1.715
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 0.000
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 1.859
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
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## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20` NA
## `myCategory.fctrmyOther:.clusterid.fctr20` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr20` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
## Pr(>|z|)
## (Intercept) 0.098576
## WordCount.log < 2e-16
## A.ratio.sum.TfIdf.nwrds 0.313717
## S.ratio.sum.TfIdf.nwrds 0.309763
## H.ratio.sum.TfIdf.nwrds 0.173873
## `myCategory.fctr#Multimedia#` 0.041485
## `myCategory.fctr#Opinion#Room For Debate` 1.36e-08
## `myCategory.fctr#Opinion#The Public Editor` 0.001911
## `myCategory.fctr#U.S.#Education` 0.989870
## `myCategory.fctrBusiness#Business Day#Dealbook` 0.286886
## `myCategory.fctrBusiness#Business Day#Small Business` 0.012218
## `myCategory.fctrBusiness#Crosswords/Games#` 5.46e-08
## `myCategory.fctrBusiness#Technology#` 0.869110
## `myCategory.fctrCulture#Arts#` 0.704581
## `myCategory.fctrForeign#World#` 0.996959
## `myCategory.fctrForeign#World#Asia Pacific` 0.039046
## `myCategory.fctrMetro#N.Y. / Region#` 0.118138
## myCategory.fctrmyOther 0.997987
## `myCategory.fctrOpEd#Opinion#` < 2e-16
## `myCategory.fctrScience#Health#` 3.18e-09
## `myCategory.fctrStyles##Fashion` 0.995822
## `myCategory.fctrStyles#U.S.#` 0.000385
## `myCategory.fctrTravel#Travel#` 0.226561
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## PubDate.day.minutes.poly.1 0.536844
## H.sum.TfIdf 0.320321
## S.sum.TfIdf 0.184903
## A.sum.TfIdf 0.190796
## `PubDate.hour.fctr(7.67,15.3]` 0.129386
## `PubDate.hour.fctr(15.3,23]` 0.885567
## H.npnct19.log 9.64e-05
## A.ratio.nstopwrds.nwrds 0.993809
## S.ratio.nstopwrds.nwrds 0.993808
## PubDate.wkend 0.492648
## H.P.recap.colon 0.999251
## H.P.quandary 0.999001
## PubDate.day.minutes.poly.4 0.143072
## PubDate.day.minutes.poly.2 0.000928
## H.P.no.comment.colon 0.017938
## S.npnct19.log 1.03e-05
## H.P.facts.figures 0.631070
## H.npnct08.log 0.031651
## PubDate.last10.log 0.503171
## PubDate.last1.log 0.172457
## H.P.readers.respond 8.81e-11
## A.T.make 0.996960
## S.T.make 0.996960
## H.ratio.nstopwrds.nwrds 0.066496
## H.T.get 0.269565
## H.npnct06.log 0.284182
## S.npnct01.log 0.635390
## A.T.can 0.993183
## H.npnct16.log 0.670801
## S.T.can 0.993176
## PubDate.day.minutes.poly.3 0.264599
## H.T.ebola 0.383577
## H.npnct01.log 0.283197
## A.T.said 0.992294
## S.T.said 0.992294
## H.T.make 0.812849
## H.npnct11.log 0.084655
## A.T.one 0.989172
## S.T.one 0.989172
## H.P.s.notebook 0.999370
## H.T.take 0.980525
## S.npnct16.log 0.869284
## A.T.presid 0.989679
## S.T.presid 0.989681
## S.npnct08.log 0.187013
## PubDate.last100.log 0.674398
## .rnorm 0.193512
## H.npnct05.log 0.999187
## H.P.friday.night.music 0.074235
## H.T.say 0.104882
## H.T.obama 0.971988
## H.T.bank 0.095469
## `PubDate.date.fctr(7,13]` 0.842572
## `PubDate.date.fctr(13,19]` 0.624662
## `PubDate.date.fctr(19,25]` 0.649737
## `PubDate.date.fctr(25,31]` 0.445214
## `PubDate.second.fctr(14.8,29.5]` 0.598439
## `PubDate.second.fctr(29.5,44.2]` 0.991350
## `PubDate.second.fctr(44.2,59.1]` 0.132509
## H.npnct07.log 0.232858
## S.npnct07.log 0.998983
## S.npnct03.log 0.998834
## A.npnct18.log 0.999626
## H.npnct12.log 0.269373
## H.T.word 0.043225
## H.T.big 0.276988
## A.npnct02.log 0.999621
## A.npnct17.log 0.992474
## S.P.year.colon 0.999573
## S.T.obama 0.988044
## A.T.obama 0.988043
## S.npnct20.log 0.998783
## H.npnct02.log 0.998802
## H.T.test 0.831619
## H.P.on.this.day 0.999097
## S.P.first.draft 0.998810
## S.T.take 0.999852
## A.T.take 0.999852
## S.npnct06.log 0.762654
## A.npnct14.log 0.698000
## S.T.time 0.992456
## A.T.time 0.992456
## H.T.newyorktim 0.468577
## H.npnct13.log 0.286038
## H.T.deal 0.996868
## S.T.new 0.997268
## A.T.new 0.997268
## H.T.billion 0.690135
## S.P.metropolitan.diary.colon 0.017520
## H.T.polit 0.478518
## H.P.verbatim.colon 0.998611
## H.T.china 0.284958
## H.T.art 0.129994
## `PubDate.minute.fctr(14.8,29.5]` 0.739267
## `PubDate.minute.fctr(29.5,44.2]` 0.434696
## `PubDate.minute.fctr(44.2,59.1]` 0.776482
## H.T.read 0.425628
## S.npnct12.log 0.445121
## H.P.today.in.politic 0.998187
## A.T.year 0.999395
## S.T.year 0.999396
## H.P.what.we.are 0.997766
## A.T.will 0.987013
## S.T.will 0.987010
## A.T.appear 0.910599
## S.T.appear NA
## PubDate.wkday.fctr1 0.229271
## PubDate.wkday.fctr2 0.054486
## PubDate.wkday.fctr3 0.227969
## PubDate.wkday.fctr4 0.084685
## PubDate.wkday.fctr5 0.202897
## PubDate.wkday.fctr6 0.062819
## H.T.pictur 0.982521
## H.T.new 0.750247
## A.T.senat 0.992727
## S.T.senat 0.992733
## S.T.show 0.998694
## A.T.show 0.998694
## H.P.today.in.smallbusiness 0.998222
## S.T.day 0.999832
## A.T.day 0.999832
## S.npnct28.log 0.997259
## A.npnct28.log 0.997127
## H.P.daily.clip.report 0.999276
## H.T.clip NA
## A.T.first 0.998164
## H.T.news 0.267747
## S.T.first 0.998165
## H.T.first 0.796800
## H.T.X2014 0.503814
## A.T.newyork 0.990875
## S.T.newyork 0.990876
## A.T.report 0.990294
## A.T.compani 0.994557
## S.T.report 0.990294
## S.T.compani 0.994556
## A.T.word 0.999477
## S.T.word 0.999477
## H.T.morn 0.998889
## H.T.busi 0.475344
## A.T.newyorktim 0.998819
## S.T.newyorktim 0.998818
## A.npnct13.log 0.991211
## A.T.share 0.041342
## S.T.share NA
## H.npnct04.log 0.070524
## S.npnct13.log 0.991277
## A.T.articl 0.997559
## S.T.articl 0.997559
## H.T.newyork 0.310857
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## H.T.today 0.328485
## H.T.springsumm 0.998095
## H.T.day 0.182336
## H.npnct14.log 0.998459
## A.T.diari 0.016581
## S.T.diari NA
## H.T.report 0.520748
## S.npnct04.log 0.064426
## H.T.daili 0.997173
## H.T.X2015 0.997800
## A.T.herald 0.994810
## S.T.herald NA
## S.npnct15.log 0.666652
## H.T.week 0.325369
## A.T.photo 0.212059
## S.T.photo NA
## A.T.intern 0.999558
## S.T.intern 0.999556
## A.T.tribun 0.996452
## S.T.tribun NA
## S.P.fashion.week 0.999844
## A.T.archiv 0.995749
## S.T.archiv NA
## H.P.fashion.week 0.994741
## H.P.year.colon 0.996985
## H.T.fashion 0.248029
## H.npnct15.log 0.001762
## A.T.fashion 0.995215
## S.T.fashion 0.995221
## A.T.week 0.999685
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## H.nstopwrds.log 0.069833
## H.npnct28.log 0.415132
## S.npnct11.log 0.146270
## S.nstopwrds.log 0.994962
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## S.nwrds.log 0.996220
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## S.nchrs.log 0.850281
## A.nwrds.unq.log 0.985519
## S.nwrds.unq.log 0.985515
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## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
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## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 0.771522
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 0.953362
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
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## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 0.142396
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## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
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## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 0.997734
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
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## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 0.999806
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 0.062988
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 0.451577
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 0.999241
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
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## `myCategory.fctrScience#Health#:.clusterid.fctr4` 0.456911
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
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## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` 0.997372
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 0.505706
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 0.998812
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` 0.762170
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 0.998168
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 0.998730
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` 0.485626
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 0.290013
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 0.101064
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` 0.755803
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 0.116005
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 0.814719
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` 0.209705
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 0.999484
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 0.999954
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 0.570226
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 0.633253
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 0.795534
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 0.307082
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` 0.396439
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr##:.clusterid.fctr18` 0.337476
## `myCategory.fctr#Multimedia#:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr18` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr18` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18` NA
## `myCategory.fctrmyOther:.clusterid.fctr18` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr18` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18` NA
## `myCategory.fctrTStyle##:.clusterid.fctr18` NA
## `myCategory.fctr##:.clusterid.fctr19` 0.504934
## `myCategory.fctr#Multimedia#:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19` NA
## `myCategory.fctrmyOther:.clusterid.fctr19` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr19` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
## `myCategory.fctrTStyle##:.clusterid.fctr19` NA
## `myCategory.fctr##:.clusterid.fctr20` 0.998761
## `myCategory.fctr#Multimedia#:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20` NA
## `myCategory.fctrmyOther:.clusterid.fctr20` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr20` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
##
## (Intercept) .
## WordCount.log ***
## A.ratio.sum.TfIdf.nwrds
## S.ratio.sum.TfIdf.nwrds
## H.ratio.sum.TfIdf.nwrds
## `myCategory.fctr#Multimedia#` *
## `myCategory.fctr#Opinion#Room For Debate` ***
## `myCategory.fctr#Opinion#The Public Editor` **
## `myCategory.fctr#U.S.#Education`
## `myCategory.fctrBusiness#Business Day#Dealbook`
## `myCategory.fctrBusiness#Business Day#Small Business` *
## `myCategory.fctrBusiness#Crosswords/Games#` ***
## `myCategory.fctrBusiness#Technology#`
## `myCategory.fctrCulture#Arts#`
## `myCategory.fctrForeign#World#`
## `myCategory.fctrForeign#World#Asia Pacific` *
## `myCategory.fctrMetro#N.Y. / Region#`
## myCategory.fctrmyOther
## `myCategory.fctrOpEd#Opinion#` ***
## `myCategory.fctrScience#Health#` ***
## `myCategory.fctrStyles##Fashion`
## `myCategory.fctrStyles#U.S.#` ***
## `myCategory.fctrTravel#Travel#`
## `myCategory.fctrTStyle##` ***
## PubDate.day.minutes.poly.1
## H.sum.TfIdf
## S.sum.TfIdf
## A.sum.TfIdf
## `PubDate.hour.fctr(7.67,15.3]`
## `PubDate.hour.fctr(15.3,23]`
## H.npnct19.log ***
## A.ratio.nstopwrds.nwrds
## S.ratio.nstopwrds.nwrds
## PubDate.wkend
## H.P.recap.colon
## H.P.quandary
## PubDate.day.minutes.poly.4
## PubDate.day.minutes.poly.2 ***
## H.P.no.comment.colon *
## S.npnct19.log ***
## H.P.facts.figures
## H.npnct08.log *
## PubDate.last10.log
## PubDate.last1.log
## H.P.readers.respond ***
## A.T.make
## S.T.make
## H.ratio.nstopwrds.nwrds .
## H.T.get
## H.npnct06.log
## S.npnct01.log
## A.T.can
## H.npnct16.log
## S.T.can
## PubDate.day.minutes.poly.3
## H.T.ebola
## H.npnct01.log
## A.T.said
## S.T.said
## H.T.make
## H.npnct11.log .
## A.T.one
## S.T.one
## H.P.s.notebook
## H.T.take
## S.npnct16.log
## A.T.presid
## S.T.presid
## S.npnct08.log
## PubDate.last100.log
## .rnorm
## H.npnct05.log
## H.P.friday.night.music .
## H.T.say
## H.T.obama
## H.T.bank .
## `PubDate.date.fctr(7,13]`
## `PubDate.date.fctr(13,19]`
## `PubDate.date.fctr(19,25]`
## `PubDate.date.fctr(25,31]`
## `PubDate.second.fctr(14.8,29.5]`
## `PubDate.second.fctr(29.5,44.2]`
## `PubDate.second.fctr(44.2,59.1]`
## H.npnct07.log
## S.npnct07.log
## S.npnct03.log
## A.npnct18.log
## H.npnct12.log
## H.T.word *
## H.T.big
## A.npnct02.log
## A.npnct17.log
## S.P.year.colon
## S.T.obama
## A.T.obama
## S.npnct20.log
## H.npnct02.log
## H.T.test
## H.P.on.this.day
## S.P.first.draft
## S.T.take
## A.T.take
## S.npnct06.log
## A.npnct14.log
## S.T.time
## A.T.time
## H.T.newyorktim
## H.npnct13.log
## H.T.deal
## S.T.new
## A.T.new
## H.T.billion
## S.P.metropolitan.diary.colon *
## H.T.polit
## H.P.verbatim.colon
## H.T.china
## H.T.art
## `PubDate.minute.fctr(14.8,29.5]`
## `PubDate.minute.fctr(29.5,44.2]`
## `PubDate.minute.fctr(44.2,59.1]`
## H.T.read
## S.npnct12.log
## H.P.today.in.politic
## A.T.year
## S.T.year
## H.P.what.we.are
## A.T.will
## S.T.will
## A.T.appear
## S.T.appear
## PubDate.wkday.fctr1
## PubDate.wkday.fctr2 .
## PubDate.wkday.fctr3
## PubDate.wkday.fctr4 .
## PubDate.wkday.fctr5
## PubDate.wkday.fctr6 .
## H.T.pictur
## H.T.new
## A.T.senat
## S.T.senat
## S.T.show
## A.T.show
## H.P.today.in.smallbusiness
## S.T.day
## A.T.day
## S.npnct28.log
## A.npnct28.log
## H.P.daily.clip.report
## H.T.clip
## A.T.first
## H.T.news
## S.T.first
## H.T.first
## H.T.X2014
## A.T.newyork
## S.T.newyork
## A.T.report
## A.T.compani
## S.T.report
## S.T.compani
## A.T.word
## S.T.word
## H.T.morn
## H.T.busi
## A.T.newyorktim
## S.T.newyorktim
## A.npnct13.log
## A.T.share *
## S.T.share
## H.npnct04.log .
## S.npnct13.log
## A.T.articl
## S.T.articl
## H.T.newyork
## PubDate.day.minutes.poly.5
## H.T.today
## H.T.springsumm
## H.T.day
## H.npnct14.log
## A.T.diari *
## S.T.diari
## H.T.report
## S.npnct04.log .
## H.T.daili
## H.T.X2015
## A.T.herald
## S.T.herald
## S.npnct15.log
## H.T.week
## A.T.photo
## S.T.photo
## A.T.intern
## S.T.intern
## A.T.tribun
## S.T.tribun
## S.P.fashion.week
## A.T.archiv
## S.T.archiv
## H.P.fashion.week
## H.P.year.colon
## H.T.fashion
## H.npnct15.log **
## A.T.fashion
## S.T.fashion
## A.T.week
## S.T.week
## H.nstopwrds.log .
## H.npnct28.log
## S.npnct11.log
## S.nstopwrds.log
## A.nstopwrds.log
## H.ndgts.log *
## S.ndgts.log .
## H.nuppr.log *
## H.nwrds.log
## H.nchrs.log
## S.nwrds.log
## A.nwrds.log
## H.nwrds.unq.log *
## S.nchrs.log
## A.nwrds.unq.log
## S.nwrds.unq.log
## S.nuppr.log **
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## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4`
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## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr18`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr18`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr18`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr18`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr18`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr18`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr18`
## `myCategory.fctrForeign#World#:.clusterid.fctr18`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18`
## `myCategory.fctrmyOther:.clusterid.fctr18`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18`
## `myCategory.fctrScience#Health#:.clusterid.fctr18`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18`
## `myCategory.fctrTStyle##:.clusterid.fctr18`
## `myCategory.fctr##:.clusterid.fctr19`
## `myCategory.fctr#Multimedia#:.clusterid.fctr19`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19`
## `myCategory.fctrForeign#World#:.clusterid.fctr19`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19`
## `myCategory.fctrmyOther:.clusterid.fctr19`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19`
## `myCategory.fctrScience#Health#:.clusterid.fctr19`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19`
## `myCategory.fctrTStyle##:.clusterid.fctr19`
## `myCategory.fctr##:.clusterid.fctr20`
## `myCategory.fctr#Multimedia#:.clusterid.fctr20`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20`
## `myCategory.fctrForeign#World#:.clusterid.fctr20`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20`
## `myCategory.fctrmyOther:.clusterid.fctr20`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20`
## `myCategory.fctrScience#Health#:.clusterid.fctr20`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20`
## `myCategory.fctrTStyle##:.clusterid.fctr20`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 1597.1 on 4193 degrees of freedom
## AIC: 2161.1
##
## Number of Fisher Scoring iterations: 21
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.6888889
## 3 0.2 0.7494357
## 4 0.3 0.7797030
## 5 0.4 0.7715602
## 6 0.5 0.7586207
## 7 0.6 0.7400151
## 8 0.7 0.7016977
## 9 0.8 0.6221441
## 10 0.9 0.4841897
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.All.X.glm.N
## 1 N 3489
## 2 Y 119
## Popular.fctr.predict.All.X.glm.Y
## 1 237
## 2 630
## Prediction
## Reference N Y
## N 3489 237
## Y 119 630
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.204469e-01 7.314774e-01 9.121295e-01 9.282106e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 1.612812e-66 5.610994e-10
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.6409432
## 3 0.2 0.6781751
## 4 0.3 0.7076101
## 5 0.4 0.7124464
## 6 0.5 0.7064220
## 7 0.6 0.6858974
## 8 0.7 0.6449400
## 9 0.8 0.5610687
## 10 0.9 0.4362851
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.All.X.glm.N
## 1 N 1607
## 2 Y 95
## Popular.fctr.predict.All.X.glm.Y
## 1 106
## 2 249
## Prediction
## Reference N Y
## N 1607 106
## Y 95 249
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.022849e-01 6.536059e-01 8.886309e-01 9.147795e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 1.211631e-19 4.805952e-01
## model_id model_method
## 1 All.X.glm glm
## feats
## 1 WordCount.log, A.ratio.sum.TfIdf.nwrds, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, H.P.recap.colon, H.P.quandary, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, H.P.no.comment.colon, S.npnct19.log, H.P.facts.figures, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.P.readers.respond, A.T.make, S.T.make, H.ratio.nstopwrds.nwrds, H.T.get, H.npnct06.log, S.npnct01.log, A.T.can, H.npnct16.log, S.T.can, PubDate.day.minutes.poly.3, H.T.ebola, H.npnct01.log, A.T.said, S.T.said, H.T.make, H.npnct11.log, A.T.one, S.T.one, H.P.s.notebook, H.T.take, S.npnct16.log, A.T.presid, S.T.presid, S.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, H.P.friday.night.music, H.T.say, H.T.obama, H.T.bank, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, H.T.word, H.T.big, A.npnct02.log, A.npnct17.log, S.P.year.colon, S.T.obama, A.T.obama, S.npnct20.log, H.npnct02.log, H.T.test, H.P.on.this.day, S.P.first.draft, S.T.take, A.T.take, S.npnct06.log, A.npnct14.log, S.T.time, A.T.time, H.T.newyorktim, H.npnct13.log, H.T.deal, S.T.new, A.T.new, H.T.billion, S.P.metropolitan.diary.colon, H.T.polit, H.P.verbatim.colon, H.T.china, H.T.art, PubDate.minute.fctr, H.T.read, S.npnct12.log, H.P.today.in.politic, A.T.year, S.T.year, H.P.what.we.are, A.T.will, S.T.will, A.T.appear, S.T.appear, PubDate.wkday.fctr, H.T.pictur, H.T.new, A.T.senat, S.T.senat, S.T.show, A.T.show, H.P.today.in.smallbusiness, S.T.day, A.T.day, S.npnct28.log, A.npnct28.log, H.P.daily.clip.report, H.T.clip, A.T.first, H.T.news, S.T.first, H.T.first, H.T.X2014, A.T.newyork, S.T.newyork, A.T.report, A.T.compani, S.T.report, S.T.compani, A.T.word, S.T.word, H.T.morn, H.T.busi, A.T.newyorktim, S.T.newyorktim, A.npnct13.log, A.T.share, S.T.share, H.npnct04.log, S.npnct13.log, A.T.articl, S.T.articl, H.T.newyork, PubDate.day.minutes.poly.5, H.T.today, H.T.springsumm, H.T.day, H.npnct14.log, A.T.diari, S.T.diari, H.T.report, S.npnct04.log, H.T.daili, H.T.X2015, A.T.herald, S.T.herald, S.npnct15.log, H.T.week, A.T.photo, S.T.photo, A.T.intern, S.T.intern, A.T.tribun, S.T.tribun, S.P.fashion.week, A.T.archiv, S.T.archiv, H.P.fashion.week, H.P.year.colon, H.T.fashion, H.npnct15.log, A.T.fashion, S.T.fashion, A.T.week, S.T.week, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 182.782 86.095
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9637398 0.3 0.779703 0.8931847
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9121295 0.9282106 0.6089725 0.9084175
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7124464 0.9022849
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.8886309 0.9147795 0.6536059 2161.079
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009143116 0.03090329
## label step_major step_minor bgn end elapsed
## 2 fit.models_1_glm 2 0 630.818 822.337 191.519
## 3 fit.models_1_rpart 3 0 822.337 NA NA
## [1] "fitting model: All.X.no.rnorm.rpart"
## [1] " indep_vars: WordCount.log, A.ratio.sum.TfIdf.nwrds, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, H.P.recap.colon, H.P.quandary, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, H.P.no.comment.colon, S.npnct19.log, H.P.facts.figures, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.P.readers.respond, A.T.make, S.T.make, H.ratio.nstopwrds.nwrds, H.T.get, H.npnct06.log, S.npnct01.log, A.T.can, H.npnct16.log, S.T.can, PubDate.day.minutes.poly.3, H.T.ebola, H.npnct01.log, A.T.said, S.T.said, H.T.make, H.npnct11.log, A.T.one, S.T.one, H.P.s.notebook, H.T.take, S.npnct16.log, A.T.presid, S.T.presid, S.npnct08.log, PubDate.last100.log, H.npnct05.log, H.P.friday.night.music, H.T.say, H.T.obama, H.T.bank, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, H.T.word, H.T.big, A.npnct02.log, A.npnct17.log, S.P.year.colon, S.T.obama, A.T.obama, S.npnct20.log, H.npnct02.log, H.T.test, H.P.on.this.day, S.P.first.draft, S.T.take, A.T.take, S.npnct06.log, A.npnct14.log, S.T.time, A.T.time, H.T.newyorktim, H.npnct13.log, H.T.deal, S.T.new, A.T.new, H.T.billion, S.P.metropolitan.diary.colon, H.T.polit, H.P.verbatim.colon, H.T.china, H.T.art, PubDate.minute.fctr, H.T.read, S.npnct12.log, H.P.today.in.politic, A.T.year, S.T.year, H.P.what.we.are, A.T.will, S.T.will, A.T.appear, S.T.appear, PubDate.wkday.fctr, H.T.pictur, H.T.new, A.T.senat, S.T.senat, S.T.show, A.T.show, H.P.today.in.smallbusiness, S.T.day, A.T.day, S.npnct28.log, A.npnct28.log, H.P.daily.clip.report, H.T.clip, A.T.first, H.T.news, S.T.first, H.T.first, H.T.X2014, A.T.newyork, S.T.newyork, A.T.report, A.T.compani, S.T.report, S.T.compani, A.T.word, S.T.word, H.T.morn, H.T.busi, A.T.newyorktim, S.T.newyorktim, A.npnct13.log, A.T.share, S.T.share, H.npnct04.log, S.npnct13.log, A.T.articl, S.T.articl, H.T.newyork, PubDate.day.minutes.poly.5, H.T.today, H.T.springsumm, H.T.day, H.npnct14.log, A.T.diari, S.T.diari, H.T.report, S.npnct04.log, H.T.daili, H.T.X2015, A.T.herald, S.T.herald, S.npnct15.log, H.T.week, A.T.photo, S.T.photo, A.T.intern, S.T.intern, A.T.tribun, S.T.tribun, S.P.fashion.week, A.T.archiv, S.T.archiv, H.P.fashion.week, H.P.year.colon, H.T.fashion, H.npnct15.log, A.T.fashion, S.T.fashion, A.T.week, S.T.week, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.034 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4475
##
## CP nsplit rel error
## 1 0.27102804 0 1.0000000
## 2 0.08411215 1 0.7289720
## 3 0.03404539 2 0.6448598
##
## Variable importance
## myCategory.fctrOpEd#Opinion#
## 51
## myCategory.fctrBusiness#Crosswords/Games#
## 17
## A.nwrds.unq.log
## 6
## S.nwrds.unq.log
## 6
## S.nchrs.log
## 6
## myCategory.fctrOpEd#Opinion#:.clusterid.fctr3
## 6
## myCategory.fctrOpEd#Opinion#:.clusterid.fctr2
## 5
## H.nchrs.log
## 1
##
## Node number 1: 4475 observations, complexity param=0.271028
## predicted class=N expected loss=0.1673743 P(node) =1
## class counts: 3726 749
## probabilities: 0.833 0.167
## left son=2 (4106 obs) right son=3 (369 obs)
## Primary splits:
## myCategory.fctrOpEd#Opinion# < 0.5 to the left, improve=297.02950, (0 missing)
## WordCount.log < 6.524296 to the left, improve=106.38390, (0 missing)
## S.nuppr.log < 1.497866 to the right, improve= 86.35796, (0 missing)
## myCategory.fctrBusiness#Crosswords/Games# < 0.5 to the left, improve= 85.77765, (0 missing)
## A.ratio.sum.TfIdf.nwrds < 1.155339 to the left, improve= 81.74534, (0 missing)
## Surrogate splits:
## A.nwrds.unq.log < 1.497866 to the right, agree=0.928, adj=0.125, (0 split)
## S.nwrds.unq.log < 1.497866 to the right, agree=0.928, adj=0.122, (0 split)
## S.nchrs.log < 3.725621 to the right, agree=0.927, adj=0.117, (0 split)
## myCategory.fctrOpEd#Opinion#:.clusterid.fctr3 < 0.5 to the left, agree=0.926, adj=0.108, (0 split)
## myCategory.fctrOpEd#Opinion#:.clusterid.fctr2 < 0.5 to the left, agree=0.926, adj=0.098, (0 split)
##
## Node number 2: 4106 observations, complexity param=0.08411215
## predicted class=N expected loss=0.1127618 P(node) =0.9175419
## class counts: 3643 463
## probabilities: 0.887 0.113
## left son=4 (4023 obs) right son=5 (83 obs)
## Primary splits:
## myCategory.fctrBusiness#Crosswords/Games# < 0.5 to the left, improve=99.60741, (0 missing)
## WordCount.log < 6.485398 to the left, improve=95.16933, (0 missing)
## myCategory.fctrScience#Health# < 0.5 to the left, improve=65.77098, (0 missing)
## myCategory.fctrStyles#U.S.# < 0.5 to the left, improve=50.94648, (0 missing)
## PubDate.day.minutes.poly.1 < 0.01628018 to the left, improve=47.34772, (0 missing)
## Surrogate splits:
## H.nchrs.log < 2.35024 to the right, agree=0.981, adj=0.060, (0 split)
## H.ratio.sum.TfIdf.nwrds < 6.393259 to the left, agree=0.980, adj=0.024, (0 split)
## H.nuppr.log < 0.8958797 to the right, agree=0.980, adj=0.024, (0 split)
## H.nwrds.log < 0.8958797 to the right, agree=0.980, adj=0.024, (0 split)
## A.T.make < 0.9262225 to the left, agree=0.980, adj=0.012, (0 split)
##
## Node number 3: 369 observations
## predicted class=Y expected loss=0.2249322 P(node) =0.0824581
## class counts: 83 286
## probabilities: 0.225 0.775
##
## Node number 4: 4023 observations
## predicted class=N expected loss=0.09694258 P(node) =0.8989944
## class counts: 3633 390
## probabilities: 0.903 0.097
##
## Node number 5: 83 observations
## predicted class=Y expected loss=0.1204819 P(node) =0.01854749
## class counts: 10 73
## probabilities: 0.120 0.880
##
## n= 4475
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4475 749 N (0.83262570 0.16737430)
## 2) myCategory.fctrOpEd#Opinion#< 0.5 4106 463 N (0.88723819 0.11276181)
## 4) myCategory.fctrBusiness#Crosswords/Games#< 0.5 4023 390 N (0.90305742 0.09694258) *
## 5) myCategory.fctrBusiness#Crosswords/Games#>=0.5 83 10 Y (0.12048193 0.87951807) *
## 3) myCategory.fctrOpEd#Opinion#>=0.5 369 83 Y (0.22493225 0.77506775) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.5978351
## 3 0.2 0.5978351
## 4 0.3 0.5978351
## 5 0.4 0.5978351
## 6 0.5 0.5978351
## 7 0.6 0.5978351
## 8 0.7 0.5978351
## 9 0.8 0.1754808
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 3633
## 2 Y 390
## Popular.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 93
## 2 359
## Prediction
## Reference N Y
## N 3633 93
## Y 390 359
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.920670e-01 5.398657e-01 8.826068e-01 9.010121e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 1.439953e-29 2.397951e-41
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.5650558
## 3 0.2 0.5650558
## 4 0.3 0.5650558
## 5 0.4 0.5650558
## 6 0.5 0.5650558
## 7 0.6 0.5650558
## 8 0.7 0.5650558
## 9 0.8 0.1562500
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 1671
## 2 Y 192
## Popular.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 42
## 2 152
## Prediction
## Reference N Y
## N 1671 42
## Y 192 152
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.862421e-01 5.054039e-01 8.717239e-01 8.996488e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 5.783557e-12 2.026854e-22
## model_id model_method
## 1 All.X.no.rnorm.rpart rpart
## feats
## 1 WordCount.log, A.ratio.sum.TfIdf.nwrds, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, H.P.recap.colon, H.P.quandary, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, H.P.no.comment.colon, S.npnct19.log, H.P.facts.figures, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.P.readers.respond, A.T.make, S.T.make, H.ratio.nstopwrds.nwrds, H.T.get, H.npnct06.log, S.npnct01.log, A.T.can, H.npnct16.log, S.T.can, PubDate.day.minutes.poly.3, H.T.ebola, H.npnct01.log, A.T.said, S.T.said, H.T.make, H.npnct11.log, A.T.one, S.T.one, H.P.s.notebook, H.T.take, S.npnct16.log, A.T.presid, S.T.presid, S.npnct08.log, PubDate.last100.log, H.npnct05.log, H.P.friday.night.music, H.T.say, H.T.obama, H.T.bank, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, H.T.word, H.T.big, A.npnct02.log, A.npnct17.log, S.P.year.colon, S.T.obama, A.T.obama, S.npnct20.log, H.npnct02.log, H.T.test, H.P.on.this.day, S.P.first.draft, S.T.take, A.T.take, S.npnct06.log, A.npnct14.log, S.T.time, A.T.time, H.T.newyorktim, H.npnct13.log, H.T.deal, S.T.new, A.T.new, H.T.billion, S.P.metropolitan.diary.colon, H.T.polit, H.P.verbatim.colon, H.T.china, H.T.art, PubDate.minute.fctr, H.T.read, S.npnct12.log, H.P.today.in.politic, A.T.year, S.T.year, H.P.what.we.are, A.T.will, S.T.will, A.T.appear, S.T.appear, PubDate.wkday.fctr, H.T.pictur, H.T.new, A.T.senat, S.T.senat, S.T.show, A.T.show, H.P.today.in.smallbusiness, S.T.day, A.T.day, S.npnct28.log, A.npnct28.log, H.P.daily.clip.report, H.T.clip, A.T.first, H.T.news, S.T.first, H.T.first, H.T.X2014, A.T.newyork, S.T.newyork, A.T.report, A.T.compani, S.T.report, S.T.compani, A.T.word, S.T.word, H.T.morn, H.T.busi, A.T.newyorktim, S.T.newyorktim, A.npnct13.log, A.T.share, S.T.share, H.npnct04.log, S.npnct13.log, A.T.articl, S.T.articl, H.T.newyork, PubDate.day.minutes.poly.5, H.T.today, H.T.springsumm, H.T.day, H.npnct14.log, A.T.diari, S.T.diari, H.T.report, S.npnct04.log, H.T.daili, H.T.X2015, A.T.herald, S.T.herald, S.npnct15.log, H.T.week, A.T.photo, S.T.photo, A.T.intern, S.T.intern, A.T.tribun, S.T.tribun, S.P.fashion.week, A.T.archiv, S.T.archiv, H.P.fashion.week, H.P.year.colon, H.T.fashion, H.npnct15.log, A.T.fashion, S.T.fashion, A.T.week, S.T.week, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 28.283 6.421
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7277461 0.7 0.5978351 0.8992181
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8826068 0.9010121 0.5798643 0.7084504
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.5650558 0.8862421
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8717239 0.8996488 0.5054039
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.008744104 0.05072074# User specified
# easier to exclude features
#model_id_pfx <- "";
# indep_vars_vctr <- setdiff(names(glb_fitobs_df),
# union(union(glb_rsp_var, glb_exclude_vars_as_features),
# c("<feat1_name>", "<feat2_name>")))
# method <- ""
# easier to include features
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
#table(glb_allobs_df$myCategory, glb_allobs_df$H.P.readers.respond, glb_allobs_df[, glb_rsp_var], useNA="ifany")
model_id <- "Rank9.Bayes.glm"; indep_vars_vctr <- c(
"myCategory.fctr*WordCount.log"
,"myCategory.fctr:.clusterid.fctr"
# ,"myCategory.fctr*H.P.readers.respond"
,"myCategory.fctr*H.npnct19.log"
# ,"myCategory.fctr*A.ratio.sum.TfIdf.nwrds"
# ,"myCategory.fctr*S.npnct13.log"
# ,"myCategory.fctr*H.npnct15.log"
# ,"myCategory.fctr*A.ratio.nstopwrds.nwrds"
# ,"myCategory.fctr*A.nuppr.log"
,"myCategory.fctr:PubDate.day.minutes.poly.1"
,"myCategory.fctr:PubDate.day.minutes.poly.2"
,"myCategory.fctr:PubDate.day.minutes.poly.3"
,"myCategory.fctr:PubDate.day.minutes.poly.4"
,"myCategory.fctr:PubDate.day.minutes.poly.5"
# ,"myCategory.fctr*H.npnct08.log"
# ,"myCategory.fctr*S.nwrds.unq.log"
# ,"myCategory.fctr:PubDate.wkend"
#,"myCategory.fctr:H.npnct28.log"
# ,"H.npnct28.log"
); method <- "glm"
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)## [1] "fitting model: Rank9.Bayes.glm.glm"
## [1] " indep_vars: myCategory.fctr*WordCount.log, myCategory.fctr:.clusterid.fctr, myCategory.fctr*H.npnct19.log, myCategory.fctr:PubDate.day.minutes.poly.1, myCategory.fctr:PubDate.day.minutes.poly.2, myCategory.fctr:PubDate.day.minutes.poly.3, myCategory.fctr:PubDate.day.minutes.poly.4, myCategory.fctr:PubDate.day.minutes.poly.5"
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: not plotting observations with leverage one:
## 121, 2403
## Warning: not plotting observations with leverage one:
## 121, 2403
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2796 -0.2588 -0.0457 0.0000 3.8405
##
## Coefficients: (315 not defined because of singularities)
## Estimate
## (Intercept) -1.341e+01
## `myCategory.fctr#Multimedia#` -2.035e+01
## `myCategory.fctr#Opinion#Room For Debate` -2.502e+02
## `myCategory.fctr#Opinion#The Public Editor` 3.482e+01
## `myCategory.fctr#U.S.#Education` -1.316e+01
## `myCategory.fctrBusiness#Business Day#Dealbook` -9.530e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 1.498e+05
## `myCategory.fctrBusiness#Crosswords/Games#` 2.203e+03
## `myCategory.fctrBusiness#Technology#` -2.256e+00
## `myCategory.fctrCulture#Arts#` 5.106e+00
## `myCategory.fctrForeign#World#` -1.316e+01
## `myCategory.fctrForeign#World#Asia Pacific` -2.748e+01
## `myCategory.fctrMetro#N.Y. / Region#` 8.850e+00
## myCategory.fctrmyOther -1.316e+01
## `myCategory.fctrOpEd#Opinion#` 1.382e+01
## `myCategory.fctrScience#Health#` 1.559e+00
## `myCategory.fctrStyles##Fashion` -1.316e+01
## `myCategory.fctrStyles#U.S.#` 8.187e+00
## `myCategory.fctrTravel#Travel#` -1.806e+02
## `myCategory.fctrTStyle##` -4.818e+01
## WordCount.log 1.788e+00
## H.npnct19.log 2.025e+00
## `myCategory.fctr#Multimedia#:WordCount.log` -6.456e-01
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` -2.778e+00
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` -2.613e+00
## `myCategory.fctr#U.S.#Education:WordCount.log` -1.788e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 1.216e+00
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` -3.663e+00
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 8.665e-01
## `myCategory.fctrBusiness#Technology#:WordCount.log` 3.151e-01
## `myCategory.fctrCulture#Arts#:WordCount.log` -9.169e-01
## `myCategory.fctrForeign#World#:WordCount.log` -1.788e+00
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` -1.658e+00
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` -1.344e+00
## `myCategory.fctrmyOther:WordCount.log` -1.788e+00
## `myCategory.fctrOpEd#Opinion#:WordCount.log` -1.604e+00
## `myCategory.fctrScience#Health#:WordCount.log` 3.320e-01
## `myCategory.fctrStyles##Fashion:WordCount.log` -1.788e+00
## `myCategory.fctrStyles#U.S.#:WordCount.log` -9.319e-01
## `myCategory.fctrTravel#Travel#:WordCount.log` 1.232e+02
## `myCategory.fctrTStyle##:WordCount.log` -3.888e-02
## `myCategory.fctr##:.clusterid.fctr2` -3.483e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 2.333e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 7.535e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` -3.944e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` -9.485e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` -3.271e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` -5.136e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` -7.266e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` -1.302e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 6.222e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` -2.323e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` -1.115e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr3` -2.837e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` -2.802e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` -2.301e+01
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 1.578e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 1.013e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 9.297e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` -2.272e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` -6.342e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 3.587e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` -1.389e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 9.688e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 1.889e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 3.296e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` -1.219e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` -2.406e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` -9.778e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` -2.310e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` -2.297e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` -5.804e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` -1.621e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 6.565e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 6.370e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 2.639e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.611e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` -1.269e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 2.175e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` -4.243e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` -2.374e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` -1.326e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` -7.132e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 2.021e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` -2.300e+01
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## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` -5.213e+04
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 4.025e-04
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` -1.892e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 5.273e+08
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` -4.345e+05
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` -1.339e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 2.025e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 1.978e-05
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` -1.147e+03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` -2.013e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` -2.884e-02
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 1.517e+02
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 3.660e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 1.873e-06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` -2.146e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` -2.546e+05
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 2.186e+03
## `myCategory.fctr##:PubDate.day.minutes.poly.4` -5.013e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` -1.122e+03
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` -1.921e+04
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 2.843e+03
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 2.610e-04
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 2.000e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` -2.325e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 2.020e+05
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` -2.335e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 1.702e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` -9.862e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` -2.360e+03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 3.355e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 1.218e-02
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` -1.013e+02
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` -7.091e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 3.147e-06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` -1.009e+02
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 8.849e+04
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` -4.228e+03
## `myCategory.fctr##:PubDate.day.minutes.poly.5` -3.890e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 1.007e+03
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 4.916e+03
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` -1.312e+04
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 8.998e-05
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` -1.659e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 1.669e+08
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` -5.268e+04
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 9.870e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 1.945e+00
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 6.945e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` -1.039e+03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` -8.700e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` -7.869e-03
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 4.981e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 2.600e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 3.928e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` -2.077e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` -6.301e+04
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 3.340e+02
## Std. Error
## (Intercept) 1.583e+00
## `myCategory.fctr#Multimedia#` 2.042e+05
## `myCategory.fctr#Opinion#Room For Debate` 5.089e+02
## `myCategory.fctr#Opinion#The Public Editor` 3.611e+07
## `myCategory.fctr#U.S.#Education` 8.892e+05
## `myCategory.fctrBusiness#Business Day#Dealbook` 3.598e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 1.057e+06
## `myCategory.fctrBusiness#Crosswords/Games#` 5.804e+08
## `myCategory.fctrBusiness#Technology#` 4.167e+00
## `myCategory.fctrCulture#Arts#` 3.355e+00
## `myCategory.fctrForeign#World#` 7.373e+05
## `myCategory.fctrForeign#World#Asia Pacific` 2.593e+01
## `myCategory.fctrMetro#N.Y. / Region#` 2.431e+00
## myCategory.fctrmyOther 7.339e+05
## `myCategory.fctrOpEd#Opinion#` 1.763e+00
## `myCategory.fctrScience#Health#` 2.899e+00
## `myCategory.fctrStyles##Fashion` 5.887e+05
## `myCategory.fctrStyles#U.S.#` 2.612e+00
## `myCategory.fctrTravel#Travel#` 1.242e+07
## `myCategory.fctrTStyle##` 7.276e+01
## WordCount.log 2.309e-01
## H.npnct19.log 4.954e-01
## `myCategory.fctr#Multimedia#:WordCount.log` 2.491e+00
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` 8.305e-01
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` 8.463e+05
## `myCategory.fctr#U.S.#Education:WordCount.log` 3.414e+04
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 5.140e-01
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` 3.132e+00
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 1.115e+00
## `myCategory.fctrBusiness#Technology#:WordCount.log` 6.289e-01
## `myCategory.fctrCulture#Arts#:WordCount.log` 5.425e-01
## `myCategory.fctrForeign#World#:WordCount.log` 5.461e+04
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` 1.406e+00
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` 3.892e-01
## `myCategory.fctrmyOther:WordCount.log` 9.043e+04
## `myCategory.fctrOpEd#Opinion#:WordCount.log` 2.620e-01
## `myCategory.fctrScience#Health#:WordCount.log` 4.622e-01
## `myCategory.fctrStyles##Fashion:WordCount.log` 9.594e+04
## `myCategory.fctrStyles#U.S.#:WordCount.log` 3.811e-01
## `myCategory.fctrTravel#Travel#:WordCount.log` 2.233e+05
## `myCategory.fctrTStyle##:WordCount.log` 6.264e-01
## `myCategory.fctr##:.clusterid.fctr2` 1.083e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 5.595e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 7.372e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 9.176e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 4.146e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 6.548e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 5.175e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 6.018e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 6.588e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 7.445e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 5.739e+04
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 4.006e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 6.528e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 5.450e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 5.183e+04
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 8.909e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 7.819e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 1.128e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 5.915e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 6.043e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 7.315e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` 8.217e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 6.425e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 9.993e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 1.177e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 4.959e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 7.002e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 8.934e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 8.606e+04
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 5.827e+04
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 5.614e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` 9.133e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 8.002e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 9.039e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 1.178e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 8.009e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 5.693e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 7.523e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` 1.232e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 6.714e+04
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 6.275e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` 6.927e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 1.219e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 7.124e+04
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` 4.218e+04
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 6.746e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 1.052e+05
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` 6.551e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 6.140e+04
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 9.348e+04
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
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## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
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## `myCategory.fctr##:PubDate.day.minutes.poly.5` 4.183e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 3.025e+07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 1.217e+04
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 2.748e+09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 2.246e+07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 1.792e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 1.046e+09
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 1.386e+10
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 1.728e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 2.543e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 7.355e+07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 7.177e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.547e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 7.884e+07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 3.423e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 3.363e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 4.739e+06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 6.776e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 1.169e+09
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 1.702e+04
## z value
## (Intercept) -8.466
## `myCategory.fctr#Multimedia#` 0.000
## `myCategory.fctr#Opinion#Room For Debate` -0.492
## `myCategory.fctr#Opinion#The Public Editor` 0.000
## `myCategory.fctr#U.S.#Education` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook` -2.649
## `myCategory.fctrBusiness#Business Day#Small Business` 0.142
## `myCategory.fctrBusiness#Crosswords/Games#` 0.000
## `myCategory.fctrBusiness#Technology#` -0.541
## `myCategory.fctrCulture#Arts#` 1.522
## `myCategory.fctrForeign#World#` 0.000
## `myCategory.fctrForeign#World#Asia Pacific` -1.060
## `myCategory.fctrMetro#N.Y. / Region#` 3.640
## myCategory.fctrmyOther 0.000
## `myCategory.fctrOpEd#Opinion#` 7.835
## `myCategory.fctrScience#Health#` 0.538
## `myCategory.fctrStyles##Fashion` 0.000
## `myCategory.fctrStyles#U.S.#` 3.134
## `myCategory.fctrTravel#Travel#` 0.000
## `myCategory.fctrTStyle##` -0.662
## WordCount.log 7.744
## H.npnct19.log 4.089
## `myCategory.fctr#Multimedia#:WordCount.log` -0.259
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` -3.344
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` 0.000
## `myCategory.fctr#U.S.#Education:WordCount.log` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 2.365
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` -1.170
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 0.777
## `myCategory.fctrBusiness#Technology#:WordCount.log` 0.501
## `myCategory.fctrCulture#Arts#:WordCount.log` -1.690
## `myCategory.fctrForeign#World#:WordCount.log` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` -1.179
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` -3.454
## `myCategory.fctrmyOther:WordCount.log` 0.000
## `myCategory.fctrOpEd#Opinion#:WordCount.log` -6.124
## `myCategory.fctrScience#Health#:WordCount.log` 0.718
## `myCategory.fctrStyles##Fashion:WordCount.log` 0.000
## `myCategory.fctrStyles#U.S.#:WordCount.log` -2.445
## `myCategory.fctrTravel#Travel#:WordCount.log` 0.001
## `myCategory.fctrTStyle##:WordCount.log` -0.062
## `myCategory.fctr##:.clusterid.fctr2` -3.215
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.417
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 1.022
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` -0.430
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` -2.288
## `myCategory.fctrScience#Health#:.clusterid.fctr2` -0.499
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` -0.992
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` -1.207
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` -0.198
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 0.836
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 0.000
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` -2.784
## `myCategory.fctrScience#Health#:.clusterid.fctr3` -0.435
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` -0.514
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 0.000
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 0.177
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 1.295
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 0.824
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` -0.384
## `myCategory.fctrScience#Health#:.clusterid.fctr4` -1.049
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 0.490
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` -1.691
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 1.508
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 0.189
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 0.280
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` -2.459
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` -0.344
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` -0.109
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 0.000
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 0.000
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` -1.034
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` -0.178
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 0.820
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 0.705
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 0.224
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 0.201
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` -0.223
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 0.289
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` -0.344
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 0.000
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` -2.113
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` -1.030
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 0.166
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 0.000
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` -0.001
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 1.227
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 0.000
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` -0.497
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 0.000
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 0.000
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` -1.000
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` -0.122
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 1.141
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` -0.206
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` -0.050
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 1.413
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` -0.405
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 0.000
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 0.000
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 0.238
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 0.144
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## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` -1.896
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` -0.292
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## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
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## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
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## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
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## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr##:.clusterid.fctr18` -1.030
## `myCategory.fctr#Multimedia#:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr18` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr18` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18` NA
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## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr18` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18` NA
## `myCategory.fctrTStyle##:.clusterid.fctr18` NA
## `myCategory.fctr##:.clusterid.fctr19` -0.196
## `myCategory.fctr#Multimedia#:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19` NA
## `myCategory.fctrmyOther:.clusterid.fctr19` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr19` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
## `myCategory.fctrTStyle##:.clusterid.fctr19` NA
## `myCategory.fctr##:.clusterid.fctr20` 0.000
## `myCategory.fctr#Multimedia#:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20` NA
## `myCategory.fctrmyOther:.clusterid.fctr20` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr20` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
## `myCategory.fctr#Multimedia#:H.npnct19.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 0.000
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 0.000
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` -0.685
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` -0.103
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 0.624
## `myCategory.fctrCulture#Arts#:H.npnct19.log` -0.387
## `myCategory.fctrForeign#World#:H.npnct19.log` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 0.381
## `myCategory.fctrmyOther:H.npnct19.log` 0.000
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` -1.606
## `myCategory.fctrScience#Health#:H.npnct19.log` -1.168
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 0.000
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` -0.477
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 0.000
## `myCategory.fctrTStyle##:H.npnct19.log` 0.000
## `myCategory.fctr##:PubDate.day.minutes.poly.1` -1.030
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 0.000
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 0.501
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 0.000
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 2.220
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 0.159
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 0.000
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 0.380
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` -0.677
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` -1.204
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 0.621
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 0.000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 1.460
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` -1.610
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 0.000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 0.025
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 0.000
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 0.019
## `myCategory.fctr##:PubDate.day.minutes.poly.2` -0.883
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 0.000
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` -0.519
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 0.000
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 1.581
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` -0.145
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 0.000
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 0.932
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 1.146
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 0.997
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` -1.015
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 0.000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` -1.778
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 1.998
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 0.000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` -1.030
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 0.000
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` -0.413
## `myCategory.fctr##:PubDate.day.minutes.poly.3` -0.995
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 0.000
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 0.483
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 0.000
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` -1.144
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 0.159
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 0.000
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` -0.826
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 0.730
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` -1.286
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` -0.359
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 0.000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 1.852
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 1.289
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 0.000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` -0.225
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 0.000
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 0.032
## `myCategory.fctr##:PubDate.day.minutes.poly.4` -1.142
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 0.000
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` -0.506
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 0.000
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 1.025
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` -0.143
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 0.000
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` -1.307
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 0.557
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` -1.531
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 0.827
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 0.000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` -1.770
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` -1.678
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 0.000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` -1.819
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 0.000
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` -0.453
## `myCategory.fctr##:PubDate.day.minutes.poly.5` -0.930
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 0.000
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 0.404
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 0.000
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` -0.926
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 0.160
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 0.000
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 0.571
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 0.077
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` -1.448
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` -0.245
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 0.000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 1.455
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 0.773
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 0.000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` -0.307
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 0.000
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 0.020
## Pr(>|z|)
## (Intercept) < 2e-16
## `myCategory.fctr#Multimedia#` 0.999920
## `myCategory.fctr#Opinion#Room For Debate` 0.622920
## `myCategory.fctr#Opinion#The Public Editor` 0.999999
## `myCategory.fctr#U.S.#Education` 0.999988
## `myCategory.fctrBusiness#Business Day#Dealbook` 0.008079
## `myCategory.fctrBusiness#Business Day#Small Business` 0.887284
## `myCategory.fctrBusiness#Crosswords/Games#` 0.999997
## `myCategory.fctrBusiness#Technology#` 0.588243
## `myCategory.fctrCulture#Arts#` 0.128017
## `myCategory.fctrForeign#World#` 0.999986
## `myCategory.fctrForeign#World#Asia Pacific` 0.289254
## `myCategory.fctrMetro#N.Y. / Region#` 0.000273
## myCategory.fctrmyOther 0.999986
## `myCategory.fctrOpEd#Opinion#` 4.68e-15
## `myCategory.fctrScience#Health#` 0.590711
## `myCategory.fctrStyles##Fashion` 0.999982
## `myCategory.fctrStyles#U.S.#` 0.001723
## `myCategory.fctrTravel#Travel#` 0.999988
## `myCategory.fctrTStyle##` 0.507869
## WordCount.log 9.61e-15
## H.npnct19.log 4.34e-05
## `myCategory.fctr#Multimedia#:WordCount.log` 0.795509
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` 0.000824
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` 0.999998
## `myCategory.fctr#U.S.#Education:WordCount.log` 0.999958
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 0.018036
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` 0.242151
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 0.437140
## `myCategory.fctrBusiness#Technology#:WordCount.log` 0.616403
## `myCategory.fctrCulture#Arts#:WordCount.log` 0.091002
## `myCategory.fctrForeign#World#:WordCount.log` 0.999974
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` 0.238334
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` 0.000552
## `myCategory.fctrmyOther:WordCount.log` 0.999984
## `myCategory.fctrOpEd#Opinion#:WordCount.log` 9.14e-10
## `myCategory.fctrScience#Health#:WordCount.log` 0.472636
## `myCategory.fctrStyles##Fashion:WordCount.log` 0.999985
## `myCategory.fctrStyles#U.S.#:WordCount.log` 0.014483
## `myCategory.fctrTravel#Travel#:WordCount.log` 0.999560
## `myCategory.fctrTStyle##:WordCount.log` 0.950504
## `myCategory.fctr##:.clusterid.fctr2` 0.001305
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.676703
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 0.306751
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## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19` NA
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19` NA
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
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## `myCategory.fctr#Multimedia#:.clusterid.fctr20` NA
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## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 1.000000
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## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 0.999957
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 0.984341
##
## (Intercept) ***
## `myCategory.fctr#Multimedia#`
## `myCategory.fctr#Opinion#Room For Debate`
## `myCategory.fctr#Opinion#The Public Editor`
## `myCategory.fctr#U.S.#Education`
## `myCategory.fctrBusiness#Business Day#Dealbook` **
## `myCategory.fctrBusiness#Business Day#Small Business`
## `myCategory.fctrBusiness#Crosswords/Games#`
## `myCategory.fctrBusiness#Technology#`
## `myCategory.fctrCulture#Arts#`
## `myCategory.fctrForeign#World#`
## `myCategory.fctrForeign#World#Asia Pacific`
## `myCategory.fctrMetro#N.Y. / Region#` ***
## myCategory.fctrmyOther
## `myCategory.fctrOpEd#Opinion#` ***
## `myCategory.fctrScience#Health#`
## `myCategory.fctrStyles##Fashion`
## `myCategory.fctrStyles#U.S.#` **
## `myCategory.fctrTravel#Travel#`
## `myCategory.fctrTStyle##`
## WordCount.log ***
## H.npnct19.log ***
## `myCategory.fctr#Multimedia#:WordCount.log`
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` ***
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log`
## `myCategory.fctr#U.S.#Education:WordCount.log`
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` *
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log`
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log`
## `myCategory.fctrBusiness#Technology#:WordCount.log`
## `myCategory.fctrCulture#Arts#:WordCount.log` .
## `myCategory.fctrForeign#World#:WordCount.log`
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log`
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` ***
## `myCategory.fctrmyOther:WordCount.log`
## `myCategory.fctrOpEd#Opinion#:WordCount.log` ***
## `myCategory.fctrScience#Health#:WordCount.log`
## `myCategory.fctrStyles##Fashion:WordCount.log`
## `myCategory.fctrStyles#U.S.#:WordCount.log` *
## `myCategory.fctrTravel#Travel#:WordCount.log`
## `myCategory.fctrTStyle##:WordCount.log`
## `myCategory.fctr##:.clusterid.fctr2` **
## `myCategory.fctr#Multimedia#:.clusterid.fctr2`
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## `myCategory.fctr##:.clusterid.fctr3`
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## `myCategory.fctr##:.clusterid.fctr4`
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## `myCategory.fctr##:.clusterid.fctr5` .
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5`
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## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5`
## `myCategory.fctrTStyle##:.clusterid.fctr5`
## `myCategory.fctr##:.clusterid.fctr6`
## `myCategory.fctr#Multimedia#:.clusterid.fctr6`
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## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1`
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1`
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1`
## `myCategory.fctr##:PubDate.day.minutes.poly.2`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2`
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2`
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2`
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2`
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` .
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` *
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2`
## `myCategory.fctr##:PubDate.day.minutes.poly.3`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3`
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3`
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3`
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3`
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` .
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3`
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3`
## `myCategory.fctr##:PubDate.day.minutes.poly.4`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4`
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4`
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4`
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4`
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` .
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` .
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` .
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4`
## `myCategory.fctr##:PubDate.day.minutes.poly.5`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5`
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5`
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5`
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5`
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5`
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5`
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 1679.8 on 4250 degrees of freedom
## AIC: 2129.8
##
## Number of Fisher Scoring iterations: 25
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.6898215
## 3 0.2 0.7419712
## 4 0.3 0.7565589
## 5 0.4 0.7550097
## 6 0.5 0.7479452
## 7 0.6 0.7274074
## 8 0.7 0.6683007
## 9 0.8 0.5963061
## 10 0.9 0.2840909
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Rank9.Bayes.glm.glm.N
## 1 N 3456
## 2 Y 129
## Popular.fctr.predict.Rank9.Bayes.glm.glm.Y
## 1 270
## 2 620
## Prediction
## Reference N Y
## N 3456 270
## Y 129 620
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.108380e-01 7.024772e-01 9.021062e-01 9.190291e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 6.467294e-52 2.404284e-12
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.6652587
## 3 0.2 0.7173913
## 4 0.3 0.7170795
## 5 0.4 0.7391911
## 6 0.5 0.7445255
## 7 0.6 0.7153966
## 8 0.7 0.6564103
## 9 0.8 0.5869159
## 10 0.9 0.3054893
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.Rank9.Bayes.glm.glm.N
## 1 N 1627
## 2 Y 89
## Popular.fctr.predict.Rank9.Bayes.glm.glm.Y
## 1 86
## 2 255
## Prediction
## Reference N Y
## N 1627 86
## Y 89 255
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.149246e-01 6.934914e-01 9.020252e-01 9.266265e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 1.697009e-27 8.798292e-01
## model_id model_method
## 1 Rank9.Bayes.glm.glm glm
## feats
## 1 myCategory.fctr*WordCount.log, myCategory.fctr:.clusterid.fctr, myCategory.fctr*H.npnct19.log, myCategory.fctr:PubDate.day.minutes.poly.1, myCategory.fctr:PubDate.day.minutes.poly.2, myCategory.fctr:PubDate.day.minutes.poly.3, myCategory.fctr:PubDate.day.minutes.poly.4, myCategory.fctr:PubDate.day.minutes.poly.5
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 246.988 106.617
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9592523 0.3 0.7565589 0.8884929
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9021062 0.9190291 0.6023604 0.9326203
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7445255 0.9149246
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.9020252 0.9266265 0.6934914 2129.829
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.005080323 0.01379869csm_mdl_id <- paste0(model_id, ".", method)
csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".", method)]]); print(head(csm_featsimp_df))## importance
## `myCategory.fctrOpEd#Opinion#` 100.00000
## WordCount.log 98.84070
## `myCategory.fctrOpEd#Opinion#:WordCount.log` 78.15739
## H.npnct19.log 52.18153
## `myCategory.fctrMetro#N.Y. / Region#` 46.45295
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` 44.08266#csm_featsimp_df[grepl("H.npnct19.log", row.names(csm_featsimp_df)), , FALSE]
#csm_OOBobs_df <- glb_get_predictions(glb_OOBobs_df, mdl_id=csm_mdl_id, rsp_var_out=glb_rsp_var_out, prob_threshold_def=glb_models_df[glb_models_df$model_id == csm_mdl_id, "opt.prob.threshold.OOB"])
#print(sprintf("%s OOB confusion matrix & accuracy: ", csm_mdl_id)); print(t(confusionMatrix(csm_OOBobs_df[, paste0(glb_rsp_var_out, csm_mdl_id)], csm_OOBobs_df[, glb_rsp_var])$table))
model_id <- "All.X.2"
indep_vars_vctr <- subset(glb_feats_df, !myNearZV &
(exclude.as.feat != 1))[, "id"]
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
indep_vars_vctr <- setdiff(indep_vars_vctr, c(NULL
,"A.nchrs.log" # correlated to "S.*"
,"A.ndgts.log" # correlated to "S.*"
,"A.nuppr.log" # correlated to "S.*"
,"A.npnct01.log" # identical to "S.npnct01.log"
,"A.npnct03.log" # correlated to "S.npnct03.log"
,"A.npnct04.log" # correlated to "S.npnct04.log"
,"A.npnct06.log" # identical to "S.npnct06.log"
,"A.npnct07.log" # identical to "S.npnct07.log"
,"A.npnct08.log" # correlated to "S.npnct08.log"
,"A.npnct11.log" # correlated to "S.*"
,"A.npnct12.log" # correlated to "S.*"
,"S.npnct14.log" # correlated to "A.*"
,"A.npnct15.log" # correlated to "S.npnct15.log"
,"A.npnct16.log" # correlated to "S.npnct16.log"
,"A.npnct19.log" # correlated to "S.*"
,"A.npnct20.log" # identical to "S.npnct20.log"
,"A.npnct21.log" # correlated to "S.npnct21.log"
,"A.P.daily.clip.report" # identical to "S.*"
,"S.P.daily.clip.report" # identical to "H.*"
,"A.P.http" # correlated to "A.npnct14.log"
,"A.P.fashion.week" # identical to "S.*"
,"H.P.first.draft" # correlated to "H.T.first"
,"A.P.first.draft" # identical to "S.*"
,"A.P.metropolitan.diary.colon" # identical to "S.*"
,"A.P.year.colon" # identical to "S.P.year.colon"
# Following features discarded in custom model
,"H.P.readers.respond"
,"S.npnct19.log", "A.npnct19.log"
,"H.ratio.sum.TfIdf.nwrds","S.ratio.sum.TfIdf.nwrds","A.ratio.sum.TfIdf.nwrds"
,"H.npnct13.log","S.npnct13.log","A.npnct13.log"
,"H.npnct15.log","S.npnct15.log","A.npnct15.log"
,"H.ratio.nstopwrds.nwrds","S.ratio.nstopwrds.nwrds","A.ratio.nstopwrds.nwrds"
,"H.nuppr.log","S.nuppr.log","A.nuppr.log"
,"H.npnct08.log","S.npnct08.log","A.npnct08.log"
,"H.nwrds.unq.log","S.nwrds.unq.log","A.nwrds.unq.log"
)); method <- "glm"
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)## [1] "fitting model: All.X.2.glm"
## [1] " indep_vars: WordCount.log, myCategory.fctr, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr, H.npnct19.log, PubDate.wkend, H.P.recap.colon, H.P.quandary, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, H.P.no.comment.colon, H.P.facts.figures, PubDate.last10.log, PubDate.last1.log, A.T.make, S.T.make, H.T.get, H.npnct06.log, S.npnct01.log, A.T.can, H.npnct16.log, S.T.can, PubDate.day.minutes.poly.3, H.T.ebola, H.npnct01.log, A.T.said, S.T.said, H.T.make, H.npnct11.log, A.T.one, S.T.one, H.P.s.notebook, H.T.take, S.npnct16.log, A.T.presid, S.T.presid, PubDate.last100.log, .rnorm, H.npnct05.log, H.P.friday.night.music, H.T.say, H.T.obama, H.T.bank, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, H.T.word, H.T.big, A.npnct02.log, A.npnct17.log, S.P.year.colon, S.T.obama, A.T.obama, S.npnct20.log, H.npnct02.log, H.T.test, H.P.on.this.day, S.P.first.draft, S.T.take, A.T.take, S.npnct06.log, A.npnct14.log, S.T.time, A.T.time, H.T.newyorktim, H.T.deal, S.T.new, A.T.new, H.T.billion, S.P.metropolitan.diary.colon, H.T.polit, H.P.verbatim.colon, H.T.china, H.T.art, PubDate.minute.fctr, H.T.read, S.npnct12.log, H.P.today.in.politic, A.T.year, S.T.year, H.P.what.we.are, A.T.will, S.T.will, A.T.appear, S.T.appear, PubDate.wkday.fctr, H.T.pictur, H.T.new, A.T.senat, S.T.senat, S.T.show, A.T.show, H.P.today.in.smallbusiness, S.T.day, A.T.day, S.npnct28.log, A.npnct28.log, H.P.daily.clip.report, H.T.clip, A.T.first, H.T.news, S.T.first, H.T.first, H.T.X2014, A.T.newyork, S.T.newyork, A.T.report, A.T.compani, S.T.report, S.T.compani, A.T.word, S.T.word, H.T.morn, H.T.busi, A.T.newyorktim, S.T.newyorktim, A.T.share, S.T.share, H.npnct04.log, A.T.articl, S.T.articl, H.T.newyork, PubDate.day.minutes.poly.5, H.T.today, H.T.springsumm, H.T.day, H.npnct14.log, A.T.diari, S.T.diari, H.T.report, S.npnct04.log, H.T.daili, H.T.X2015, A.T.herald, S.T.herald, H.T.week, A.T.photo, S.T.photo, A.T.intern, S.T.intern, A.T.tribun, S.T.tribun, S.P.fashion.week, A.T.archiv, S.T.archiv, H.P.fashion.week, H.P.year.colon, H.T.fashion, A.T.fashion, S.T.fashion, A.T.week, S.T.week, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, S.nchrs.log, myCategory.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: not plotting observations with leverage one:
## 129, 1163, 3511, 3846, 3953
## Warning: not plotting observations with leverage one:
## 129, 1163, 3511, 3846, 3953
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.0736 -0.2911 -0.0787 0.0000 3.2685
##
## Coefficients: (321 not defined because of singularities)
## Estimate
## (Intercept) -1.125e+00
## WordCount.log 1.196e+00
## `myCategory.fctr#Multimedia#` -2.157e+00
## `myCategory.fctr#Opinion#Room For Debate` -2.896e+00
## `myCategory.fctr#Opinion#The Public Editor` 3.591e+00
## `myCategory.fctr#U.S.#Education` -1.904e+01
## `myCategory.fctrBusiness#Business Day#Dealbook` -4.716e-01
## `myCategory.fctrBusiness#Business Day#Small Business` -1.540e+00
## `myCategory.fctrBusiness#Crosswords/Games#` 2.643e+00
## `myCategory.fctrBusiness#Technology#` 1.774e-02
## `myCategory.fctrCulture#Arts#` -7.754e-01
## `myCategory.fctrForeign#World#` -1.628e+01
## `myCategory.fctrForeign#World#Asia Pacific` -1.787e+00
## `myCategory.fctrMetro#N.Y. / Region#` 8.349e-01
## myCategory.fctrmyOther -1.857e+01
## `myCategory.fctrOpEd#Opinion#` 3.383e+00
## `myCategory.fctrScience#Health#` 2.575e+00
## `myCategory.fctrStyles##Fashion` -2.023e+01
## `myCategory.fctrStyles#U.S.#` 2.216e+00
## `myCategory.fctrTravel#Travel#` -1.716e+00
## `myCategory.fctrTStyle##` -1.981e+00
## PubDate.day.minutes.poly.1 6.788e+00
## H.sum.TfIdf -4.091e-02
## S.sum.TfIdf -6.173e-01
## A.sum.TfIdf 5.110e-01
## `PubDate.hour.fctr(7.67,15.3]` 6.295e-01
## `PubDate.hour.fctr(15.3,23]` 2.059e-02
## H.npnct19.log 1.503e+00
## PubDate.wkend -3.231e-01
## H.P.recap.colon -1.987e+01
## H.P.quandary 2.235e+01
## PubDate.day.minutes.poly.4 -1.550e+01
## PubDate.day.minutes.poly.2 3.952e+01
## H.P.no.comment.colon 1.821e+00
## H.P.facts.figures 1.215e-01
## PubDate.last10.log 1.126e-01
## PubDate.last1.log -4.189e-02
## A.T.make -2.832e+02
## S.T.make 2.818e+02
## H.T.get 4.655e-01
## H.npnct06.log 9.617e-01
## S.npnct01.log 2.604e-02
## A.T.can 1.150e+02
## H.npnct16.log -5.118e-01
## S.T.can -1.161e+02
## PubDate.day.minutes.poly.3 7.618e+00
## H.T.ebola -6.902e-01
## H.npnct01.log -1.550e+00
## A.T.said 2.046e+03
## S.T.said -2.045e+03
## H.T.make -2.296e-01
## H.npnct11.log 4.115e-01
## A.T.one 1.422e+03
## S.T.one -1.421e+03
## H.P.s.notebook -1.765e+01
## H.T.take -3.256e-02
## S.npnct16.log -3.120e-01
## A.T.presid 1.628e+03
## S.T.presid -1.627e+03
## PubDate.last100.log 1.352e-02
## .rnorm -6.953e-02
## H.npnct05.log -2.577e+01
## H.P.friday.night.music -2.789e+00
## H.T.say -7.837e-01
## H.T.obama -6.650e-02
## H.T.bank 1.210e+00
## `PubDate.date.fctr(7,13]` 4.779e-02
## `PubDate.date.fctr(13,19]` -2.085e-01
## `PubDate.date.fctr(19,25]` -1.405e-01
## `PubDate.date.fctr(25,31]` 8.847e-02
## `PubDate.second.fctr(14.8,29.5]` 2.538e-02
## `PubDate.second.fctr(29.5,44.2]` -1.727e-02
## `PubDate.second.fctr(44.2,59.1]` -2.812e-01
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## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` -1.666e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 1.275e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` -1.691e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` -6.741e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` -1.098e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 4.963e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 4.839e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` -5.618e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 8.206e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` 8.291e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr##:.clusterid.fctr18` -1.450e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr18` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr18` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18` NA
## `myCategory.fctrmyOther:.clusterid.fctr18` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr18` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18` NA
## `myCategory.fctrTStyle##:.clusterid.fctr18` NA
## `myCategory.fctr##:.clusterid.fctr19` -4.829e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19` NA
## `myCategory.fctrmyOther:.clusterid.fctr19` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr19` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
## `myCategory.fctrTStyle##:.clusterid.fctr19` NA
## `myCategory.fctr##:.clusterid.fctr20` -1.922e+01
## `myCategory.fctr#Multimedia#:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20` NA
## `myCategory.fctrmyOther:.clusterid.fctr20` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr20` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
## Std. Error
## (Intercept) 2.406e+00
## WordCount.log 1.022e-01
## `myCategory.fctr#Multimedia#` 8.460e-01
## `myCategory.fctr#Opinion#Room For Debate` 6.031e-01
## `myCategory.fctr#Opinion#The Public Editor` 1.262e+00
## `myCategory.fctr#U.S.#Education` 9.940e+02
## `myCategory.fctrBusiness#Business Day#Dealbook` 4.026e-01
## `myCategory.fctrBusiness#Business Day#Small Business` 7.210e-01
## `myCategory.fctrBusiness#Crosswords/Games#` 5.448e-01
## `myCategory.fctrBusiness#Technology#` 7.866e-01
## `myCategory.fctrCulture#Arts#` 5.946e-01
## `myCategory.fctrForeign#World#` 1.746e+03
## `myCategory.fctrForeign#World#Asia Pacific` 7.011e-01
## `myCategory.fctrMetro#N.Y. / Region#` 6.035e-01
## myCategory.fctrmyOther 3.094e+03
## `myCategory.fctrOpEd#Opinion#` 3.770e-01
## `myCategory.fctrScience#Health#` 4.390e-01
## `myCategory.fctrStyles##Fashion` 1.510e+03
## `myCategory.fctrStyles#U.S.#` 4.683e-01
## `myCategory.fctrTravel#Travel#` 1.070e+00
## `myCategory.fctrTStyle##` 5.508e-01
## PubDate.day.minutes.poly.1 1.706e+01
## H.sum.TfIdf 7.473e-02
## S.sum.TfIdf 5.537e+00
## A.sum.TfIdf 5.541e+00
## `PubDate.hour.fctr(7.67,15.3]` 5.124e-01
## `PubDate.hour.fctr(15.3,23]` 5.847e-01
## H.npnct19.log 3.427e-01
## PubDate.wkend 4.693e-01
## H.P.recap.colon 8.926e+03
## H.P.quandary 7.502e+03
## PubDate.day.minutes.poly.4 1.061e+01
## PubDate.day.minutes.poly.2 1.300e+01
## H.P.no.comment.colon 1.214e+00
## H.P.facts.figures 1.586e+00
## PubDate.last10.log 1.373e-01
## PubDate.last1.log 4.640e-02
## A.T.make 3.797e+06
## S.T.make 3.793e+06
## H.T.get 4.353e-01
## H.npnct06.log 1.265e+00
## S.npnct01.log 1.780e+00
## A.T.can 5.885e+04
## H.npnct16.log 1.269e+00
## S.T.can 5.885e+04
## PubDate.day.minutes.poly.3 7.016e+00
## H.T.ebola 4.184e-01
## H.npnct01.log 1.331e+00
## A.T.said 1.175e+06
## S.T.said 1.175e+06
## H.T.make 4.098e-01
## H.npnct11.log 2.176e-01
## A.T.one 5.545e+05
## S.T.one 5.540e+05
## H.P.s.notebook 8.603e+03
## H.T.take 5.166e-01
## S.npnct16.log 1.399e+00
## A.T.presid 2.075e+05
## S.T.presid 2.075e+05
## PubDate.last100.log 4.659e-02
## .rnorm 6.565e-02
## H.npnct05.log 1.021e+04
## H.P.friday.night.music 1.258e+00
## H.T.say 5.140e-01
## H.T.obama 6.808e-01
## H.T.bank 8.160e-01
## `PubDate.date.fctr(7,13]` 2.080e-01
## `PubDate.date.fctr(13,19]` 2.061e-01
## `PubDate.date.fctr(19,25]` 1.997e-01
## `PubDate.date.fctr(25,31]` 2.151e-01
## `PubDate.second.fctr(14.8,29.5]` 1.847e-01
## `PubDate.second.fctr(29.5,44.2]` 1.804e-01
## `PubDate.second.fctr(44.2,59.1]` 1.871e-01
## H.npnct07.log 2.151e-01
## S.npnct07.log 9.678e+03
## S.npnct03.log 8.786e+03
## A.npnct18.log 3.791e+04
## H.npnct12.log 3.352e-01
## H.T.word 8.645e-01
## H.T.big 6.943e-01
## A.npnct02.log 1.534e+04
## A.npnct17.log 1.049e+04
## S.P.year.colon 4.601e+03
## S.T.obama 1.507e+05
## A.T.obama 1.507e+05
## S.npnct20.log 7.509e+03
## H.npnct02.log 5.054e+03
## H.T.test 8.483e-01
## H.P.on.this.day 5.548e+03
## S.P.first.draft 4.696e+03
## S.T.take 3.590e+05
## A.T.take 3.594e+05
## S.npnct06.log 1.813e+00
## A.npnct14.log 1.951e+00
## S.T.time 2.424e+06
## A.T.time 2.427e+06
## H.T.newyorktim 8.374e-01
## H.T.deal 2.499e+03
## S.T.new 5.243e+06
## A.T.new 5.246e+06
## H.T.billion 1.611e+00
## S.P.metropolitan.diary.colon 3.892e+00
## H.T.polit 7.340e-01
## H.P.verbatim.colon 3.704e+03
## H.T.china 1.137e+00
## H.T.art 1.102e+00
## `PubDate.minute.fctr(14.8,29.5]` 1.918e-01
## `PubDate.minute.fctr(29.5,44.2]` 1.884e-01
## `PubDate.minute.fctr(44.2,59.1]` 1.962e-01
## H.T.read 6.368e-01
## S.npnct12.log 2.216e-01
## H.P.today.in.politic 3.246e+03
## A.T.year 1.658e+06
## S.T.year 1.658e+06
## H.P.what.we.are 2.696e+03
## A.T.will 6.836e+04
## S.T.will 6.828e+04
## A.T.appear 1.290e+00
## S.T.appear NA
## PubDate.wkday.fctr1 5.576e-01
## PubDate.wkday.fctr2 6.105e-01
## PubDate.wkday.fctr3 6.009e-01
## PubDate.wkday.fctr4 5.955e-01
## PubDate.wkday.fctr5 6.013e-01
## PubDate.wkday.fctr6 5.084e-01
## H.T.pictur 6.507e-01
## H.T.new 5.538e-01
## A.T.senat 8.989e+04
## S.T.senat 8.978e+04
## S.T.show 4.265e+06
## A.T.show 4.265e+06
## H.P.today.in.smallbusiness 2.838e+03
## S.T.day 2.960e+05
## A.T.day 2.966e+05
## S.npnct28.log 1.566e+05
## A.npnct28.log 1.565e+05
## H.P.daily.clip.report 1.878e+04
## H.T.clip NA
## A.T.first 1.049e+06
## H.T.news 8.030e-01
## S.T.first 1.049e+06
## H.T.first 1.165e+00
## H.T.X2014 1.192e+00
## A.T.newyork 1.708e+06
## S.T.newyork 1.708e+06
## A.T.report 7.569e+05
## A.T.compani 1.627e+05
## S.T.report 7.569e+05
## S.T.compani 1.627e+05
## A.T.word 3.549e+06
## S.T.word 3.549e+06
## H.T.morn 2.360e+03
## H.T.busi 8.171e-01
## A.T.newyorktim 2.013e+06
## S.T.newyorktim 2.010e+06
## A.T.share 1.227e+00
## S.T.share NA
## H.npnct04.log 1.337e+00
## A.T.articl 1.958e+06
## S.T.articl 1.958e+06
## H.T.newyork 6.572e-01
## PubDate.day.minutes.poly.5 7.223e+00
## H.T.today 7.757e-01
## H.T.springsumm 5.309e+03
## H.T.day 6.681e-01
## H.npnct14.log 4.966e+03
## A.T.diari 8.317e+00
## S.T.diari NA
## H.T.report 1.030e+00
## S.npnct04.log 6.872e-01
## H.T.daili 3.181e+03
## H.T.X2015 4.302e+03
## A.T.herald 4.267e+03
## S.T.herald NA
## H.T.week 7.448e-01
## A.T.photo 2.298e+00
## S.T.photo NA
## A.T.intern 8.502e+05
## S.T.intern 8.502e+05
## A.T.tribun 4.340e+03
## S.T.tribun NA
## S.P.fashion.week 1.197e+03
## A.T.archiv 3.777e+03
## S.T.archiv NA
## H.P.fashion.week 9.873e+02
## H.P.year.colon 1.765e+03
## H.T.fashion 1.651e+00
## A.T.fashion 5.152e+06
## S.T.fashion 5.152e+06
## A.T.week 1.061e+06
## S.T.week 1.061e+06
## H.nstopwrds.log 1.778e-01
## H.npnct28.log 1.968e+00
## S.npnct11.log 1.519e-01
## S.nstopwrds.log 6.019e+01
## A.nstopwrds.log 6.020e+01
## H.ndgts.log 2.834e-01
## S.ndgts.log 1.675e-01
## H.nwrds.log 6.136e-01
## H.nchrs.log 4.767e-01
## S.nwrds.log 5.900e+01
## A.nwrds.log 5.903e+01
## S.nchrs.log 7.565e-01
## `myCategory.fctr##:.clusterid.fctr2` 1.641e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 5.975e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 8.493e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 9.538e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 6.057e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 6.287e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 5.837e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 6.666e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
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## `myCategory.fctrBusiness#Technology#:.clusterid.fctr18` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18` NA
## `myCategory.fctrmyOther:.clusterid.fctr18` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr18` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18` NA
## `myCategory.fctrTStyle##:.clusterid.fctr18` NA
## `myCategory.fctr##:.clusterid.fctr19` 1.666e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19` NA
## `myCategory.fctrmyOther:.clusterid.fctr19` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr19` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
## `myCategory.fctrTStyle##:.clusterid.fctr19` NA
## `myCategory.fctr##:.clusterid.fctr20` 5.372e+03
## `myCategory.fctr#Multimedia#:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20` NA
## `myCategory.fctrmyOther:.clusterid.fctr20` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr20` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
## z value
## (Intercept) -0.468
## WordCount.log 11.701
## `myCategory.fctr#Multimedia#` -2.550
## `myCategory.fctr#Opinion#Room For Debate` -4.801
## `myCategory.fctr#Opinion#The Public Editor` 2.846
## `myCategory.fctr#U.S.#Education` -0.019
## `myCategory.fctrBusiness#Business Day#Dealbook` -1.171
## `myCategory.fctrBusiness#Business Day#Small Business` -2.136
## `myCategory.fctrBusiness#Crosswords/Games#` 4.851
## `myCategory.fctrBusiness#Technology#` 0.023
## `myCategory.fctrCulture#Arts#` -1.304
## `myCategory.fctrForeign#World#` -0.009
## `myCategory.fctrForeign#World#Asia Pacific` -2.549
## `myCategory.fctrMetro#N.Y. / Region#` 1.384
## myCategory.fctrmyOther -0.006
## `myCategory.fctrOpEd#Opinion#` 8.974
## `myCategory.fctrScience#Health#` 5.866
## `myCategory.fctrStyles##Fashion` -0.013
## `myCategory.fctrStyles#U.S.#` 4.733
## `myCategory.fctrTravel#Travel#` -1.604
## `myCategory.fctrTStyle##` -3.597
## PubDate.day.minutes.poly.1 0.398
## H.sum.TfIdf -0.548
## S.sum.TfIdf -0.111
## A.sum.TfIdf 0.092
## `PubDate.hour.fctr(7.67,15.3]` 1.228
## `PubDate.hour.fctr(15.3,23]` 0.035
## H.npnct19.log 4.387
## PubDate.wkend -0.688
## H.P.recap.colon -0.002
## H.P.quandary 0.003
## PubDate.day.minutes.poly.4 -1.461
## PubDate.day.minutes.poly.2 3.039
## H.P.no.comment.colon 1.500
## H.P.facts.figures 0.077
## PubDate.last10.log 0.820
## PubDate.last1.log -0.903
## A.T.make 0.000
## S.T.make 0.000
## H.T.get 1.069
## H.npnct06.log 0.760
## S.npnct01.log 0.015
## A.T.can 0.002
## H.npnct16.log -0.403
## S.T.can -0.002
## PubDate.day.minutes.poly.3 1.086
## H.T.ebola -1.649
## H.npnct01.log -1.165
## A.T.said 0.002
## S.T.said -0.002
## H.T.make -0.560
## H.npnct11.log 1.891
## A.T.one 0.003
## S.T.one -0.003
## H.P.s.notebook -0.002
## H.T.take -0.063
## S.npnct16.log -0.223
## A.T.presid 0.008
## S.T.presid -0.008
## PubDate.last100.log 0.290
## .rnorm -1.059
## H.npnct05.log -0.003
## H.P.friday.night.music -2.217
## H.T.say -1.525
## H.T.obama -0.098
## H.T.bank 1.483
## `PubDate.date.fctr(7,13]` 0.230
## `PubDate.date.fctr(13,19]` -1.012
## `PubDate.date.fctr(19,25]` -0.704
## `PubDate.date.fctr(25,31]` 0.411
## `PubDate.second.fctr(14.8,29.5]` 0.137
## `PubDate.second.fctr(29.5,44.2]` -0.096
## `PubDate.second.fctr(44.2,59.1]` -1.503
## H.npnct07.log 0.438
## S.npnct07.log -0.003
## S.npnct03.log -0.004
## A.npnct18.log 0.000
## H.npnct12.log 1.482
## H.T.word 1.718
## H.T.big -1.370
## A.npnct02.log -0.001
## A.npnct17.log 0.000
## S.P.year.colon 0.001
## S.T.obama -0.007
## A.T.obama 0.007
## S.npnct20.log -0.003
## H.npnct02.log -0.004
## H.T.test 0.360
## H.P.on.this.day -0.003
## S.P.first.draft -0.004
## S.T.take 0.003
## A.T.take -0.003
## S.npnct06.log -0.285
## A.npnct14.log 0.167
## S.T.time 0.000
## A.T.time 0.000
## H.T.newyorktim -0.370
## H.T.deal -0.009
## S.T.new 0.000
## A.T.new 0.000
## H.T.billion 0.373
## S.P.metropolitan.diary.colon -2.195
## H.T.polit 1.004
## H.P.verbatim.colon -0.004
## H.T.china -1.389
## H.T.art -1.533
## `PubDate.minute.fctr(14.8,29.5]` 0.071
## `PubDate.minute.fctr(29.5,44.2]` -0.971
## `PubDate.minute.fctr(44.2,59.1]` 0.169
## H.T.read -0.558
## S.npnct12.log -0.406
## H.P.today.in.politic -0.005
## A.T.year 0.001
## S.T.year -0.001
## H.P.what.we.are -0.007
## A.T.will 0.004
## S.T.will -0.004
## A.T.appear -0.038
## S.T.appear NA
## PubDate.wkday.fctr1 -1.123
## PubDate.wkday.fctr2 -1.866
## PubDate.wkday.fctr3 -1.146
## PubDate.wkday.fctr4 -1.557
## PubDate.wkday.fctr5 -1.108
## PubDate.wkday.fctr6 -1.892
## H.T.pictur -0.265
## H.T.new -0.723
## A.T.senat 0.007
## S.T.senat -0.007
## S.T.show 0.000
## A.T.show 0.000
## H.P.today.in.smallbusiness -0.005
## S.T.day -0.003
## A.T.day 0.003
## S.npnct28.log -0.002
## A.npnct28.log 0.002
## H.P.daily.clip.report 0.002
## H.T.clip NA
## A.T.first -0.001
## H.T.news -0.852
## S.T.first 0.001
## H.T.first -0.135
## H.T.X2014 -0.074
## A.T.newyork 0.001
## S.T.newyork -0.001
## A.T.report -0.002
## A.T.compani 0.002
## S.T.report 0.002
## S.T.compani -0.002
## A.T.word 0.001
## S.T.word -0.001
## H.T.morn 0.006
## H.T.busi -0.873
## A.T.newyorktim 0.000
## S.T.newyorktim 0.000
## A.T.share -1.619
## S.T.share NA
## H.npnct04.log -1.772
## A.T.articl 0.001
## S.T.articl -0.001
## H.T.newyork -0.927
## PubDate.day.minutes.poly.5 -0.583
## H.T.today -1.727
## H.T.springsumm 0.005
## H.T.day -1.658
## H.npnct14.log -0.004
## A.T.diari 2.146
## S.T.diari NA
## H.T.report -0.899
## S.npnct04.log -1.462
## H.T.daili -0.009
## H.T.X2015 -0.006
## A.T.herald 0.016
## S.T.herald NA
## H.T.week -1.292
## A.T.photo -1.152
## S.T.photo NA
## A.T.intern 0.001
## S.T.intern -0.001
## A.T.tribun -0.011
## S.T.tribun NA
## S.P.fashion.week 0.000
## A.T.archiv -0.013
## S.T.archiv NA
## H.P.fashion.week -0.014
## H.P.year.colon -0.009
## H.T.fashion 1.010
## A.T.fashion 0.000
## S.T.fashion 0.000
## A.T.week -0.001
## S.T.week 0.001
## H.nstopwrds.log -1.238
## H.npnct28.log -0.280
## S.npnct11.log -1.387
## S.nstopwrds.log -0.785
## A.nstopwrds.log 0.798
## H.ndgts.log 1.438
## S.ndgts.log -1.462
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## A.nwrds.log -0.440
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## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.881
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 0.424
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 0.196
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` -0.342
## `myCategory.fctrScience#Health#:.clusterid.fctr2` -0.296
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 0.022
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` -0.772
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` -1.003
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 1.068
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` -0.006
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` -0.214
## `myCategory.fctrScience#Health#:.clusterid.fctr3` -0.503
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` -1.474
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` -0.001
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 1.913
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 1.092
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 0.002
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 0.456
## `myCategory.fctrScience#Health#:.clusterid.fctr4` -0.409
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 0.225
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` -0.638
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 1.085
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` -0.121
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 0.459
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` -1.019
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 0.170
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` -0.256
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
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## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` -0.006
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
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## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` -1.381
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` -0.834
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 0.573
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 0.773
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 0.452
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
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## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 0.456
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` -0.244
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` 0.023
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` -0.006
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` -1.717
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` -0.158
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 0.155
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` -0.005
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` -0.008
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 0.782
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` -0.003
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` -0.262
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` -0.006
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## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` -0.004
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` -0.990
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` -0.923
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 1.541
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` 0.406
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` -1.391
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 0.900
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` -1.243
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` -0.002
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 0.000
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 0.742
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 0.478
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` -0.353
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 0.655
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` 0.926
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr##:.clusterid.fctr18` -1.015
## `myCategory.fctr#Multimedia#:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr18` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr18` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18` NA
## `myCategory.fctrmyOther:.clusterid.fctr18` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr18` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18` NA
## `myCategory.fctrTStyle##:.clusterid.fctr18` NA
## `myCategory.fctr##:.clusterid.fctr19` -0.290
## `myCategory.fctr#Multimedia#:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19` NA
## `myCategory.fctrmyOther:.clusterid.fctr19` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr19` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
## `myCategory.fctrTStyle##:.clusterid.fctr19` NA
## `myCategory.fctr##:.clusterid.fctr20` -0.004
## `myCategory.fctr#Multimedia#:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20` NA
## `myCategory.fctrmyOther:.clusterid.fctr20` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr20` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
## Pr(>|z|)
## (Intercept) 0.640133
## WordCount.log < 2e-16
## `myCategory.fctr#Multimedia#` 0.010783
## `myCategory.fctr#Opinion#Room For Debate` 1.58e-06
## `myCategory.fctr#Opinion#The Public Editor` 0.004426
## `myCategory.fctr#U.S.#Education` 0.984721
## `myCategory.fctrBusiness#Business Day#Dealbook` 0.241542
## `myCategory.fctrBusiness#Business Day#Small Business` 0.032641
## `myCategory.fctrBusiness#Crosswords/Games#` 1.23e-06
## `myCategory.fctrBusiness#Technology#` 0.982007
## `myCategory.fctrCulture#Arts#` 0.192149
## `myCategory.fctrForeign#World#` 0.992562
## `myCategory.fctrForeign#World#Asia Pacific` 0.010806
## `myCategory.fctrMetro#N.Y. / Region#` 0.166485
## myCategory.fctrmyOther 0.995210
## `myCategory.fctrOpEd#Opinion#` < 2e-16
## `myCategory.fctrScience#Health#` 4.47e-09
## `myCategory.fctrStyles##Fashion` 0.989311
## `myCategory.fctrStyles#U.S.#` 2.22e-06
## `myCategory.fctrTravel#Travel#` 0.108753
## `myCategory.fctrTStyle##` 0.000322
## PubDate.day.minutes.poly.1 0.690748
## H.sum.TfIdf 0.584030
## S.sum.TfIdf 0.911229
## A.sum.TfIdf 0.926513
## `PubDate.hour.fctr(7.67,15.3]` 0.219276
## `PubDate.hour.fctr(15.3,23]` 0.971907
## H.npnct19.log 1.15e-05
## PubDate.wkend 0.491197
## H.P.recap.colon 0.998224
## H.P.quandary 0.997623
## PubDate.day.minutes.poly.4 0.144038
## PubDate.day.minutes.poly.2 0.002376
## H.P.no.comment.colon 0.133724
## H.P.facts.figures 0.938916
## PubDate.last10.log 0.411978
## PubDate.last1.log 0.366638
## A.T.make 0.999940
## S.T.make 0.999941
## H.T.get 0.284913
## H.npnct06.log 0.446982
## S.npnct01.log 0.988331
## A.T.can 0.998441
## H.npnct16.log 0.686788
## S.T.can 0.998426
## PubDate.day.minutes.poly.3 0.277588
## H.T.ebola 0.099055
## H.npnct01.log 0.243932
## A.T.said 0.998611
## S.T.said 0.998611
## H.T.make 0.575269
## H.npnct11.log 0.058585
## A.T.one 0.997954
## S.T.one 0.997953
## H.P.s.notebook 0.998363
## H.T.take 0.949745
## S.npnct16.log 0.823525
## A.T.presid 0.993742
## S.T.presid 0.993744
## PubDate.last100.log 0.771651
## .rnorm 0.289609
## H.npnct05.log 0.997985
## H.P.friday.night.music 0.026602
## H.T.say 0.127342
## H.T.obama 0.922180
## H.T.bank 0.138050
## `PubDate.date.fctr(7,13]` 0.818253
## `PubDate.date.fctr(13,19]` 0.311609
## `PubDate.date.fctr(19,25]` 0.481623
## `PubDate.date.fctr(25,31]` 0.680844
## `PubDate.second.fctr(14.8,29.5]` 0.890697
## `PubDate.second.fctr(29.5,44.2]` 0.923731
## `PubDate.second.fctr(44.2,59.1]` 0.132869
## H.npnct07.log 0.661397
## S.npnct07.log 0.997285
## S.npnct03.log 0.997202
## A.npnct18.log 0.999979
## H.npnct12.log 0.138229
## H.T.word 0.085722
## H.T.big 0.170782
## A.npnct02.log 0.999100
## A.npnct17.log 0.999899
## S.P.year.colon 0.999413
## S.T.obama 0.994437
## A.T.obama 0.994436
## S.npnct20.log 0.997277
## H.npnct02.log 0.997039
## H.T.test 0.718894
## H.P.on.this.day 0.997659
## S.P.first.draft 0.997180
## S.T.take 0.997655
## A.T.take 0.997654
## S.npnct06.log 0.775742
## A.npnct14.log 0.867684
## S.T.time 0.999868
## A.T.time 0.999869
## H.T.newyorktim 0.711146
## H.T.deal 0.992496
## S.T.new 0.999813
## A.T.new 0.999813
## H.T.billion 0.709161
## S.P.metropolitan.diary.colon 0.028158
## H.T.polit 0.315492
## H.P.verbatim.colon 0.996587
## H.T.china 0.164852
## H.T.art 0.125359
## `PubDate.minute.fctr(14.8,29.5]` 0.943625
## `PubDate.minute.fctr(29.5,44.2]` 0.331644
## `PubDate.minute.fctr(44.2,59.1]` 0.866179
## H.T.read 0.576926
## S.npnct12.log 0.684502
## H.P.today.in.politic 0.995830
## A.T.year 0.998858
## S.T.year 0.998857
## H.P.what.we.are 0.994132
## A.T.will 0.997082
## S.T.will 0.997075
## A.T.appear 0.969778
## S.T.appear NA
## PubDate.wkday.fctr1 0.261543
## PubDate.wkday.fctr2 0.062107
## PubDate.wkday.fctr3 0.251822
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## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 0.723903
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 0.512519
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` 0.354567
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr##:.clusterid.fctr18` 0.310092
## `myCategory.fctr#Multimedia#:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr18` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr18` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18` NA
## `myCategory.fctrmyOther:.clusterid.fctr18` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr18` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18` NA
## `myCategory.fctrTStyle##:.clusterid.fctr18` NA
## `myCategory.fctr##:.clusterid.fctr19` 0.771908
## `myCategory.fctr#Multimedia#:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19` NA
## `myCategory.fctrmyOther:.clusterid.fctr19` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr19` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
## `myCategory.fctrTStyle##:.clusterid.fctr19` NA
## `myCategory.fctr##:.clusterid.fctr20` 0.997145
## `myCategory.fctr#Multimedia#:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20` NA
## `myCategory.fctrmyOther:.clusterid.fctr20` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr20` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
##
## (Intercept)
## WordCount.log ***
## `myCategory.fctr#Multimedia#` *
## `myCategory.fctr#Opinion#Room For Debate` ***
## `myCategory.fctr#Opinion#The Public Editor` **
## `myCategory.fctr#U.S.#Education`
## `myCategory.fctrBusiness#Business Day#Dealbook`
## `myCategory.fctrBusiness#Business Day#Small Business` *
## `myCategory.fctrBusiness#Crosswords/Games#` ***
## `myCategory.fctrBusiness#Technology#`
## `myCategory.fctrCulture#Arts#`
## `myCategory.fctrForeign#World#`
## `myCategory.fctrForeign#World#Asia Pacific` *
## `myCategory.fctrMetro#N.Y. / Region#`
## myCategory.fctrmyOther
## `myCategory.fctrOpEd#Opinion#` ***
## `myCategory.fctrScience#Health#` ***
## `myCategory.fctrStyles##Fashion`
## `myCategory.fctrStyles#U.S.#` ***
## `myCategory.fctrTravel#Travel#`
## `myCategory.fctrTStyle##` ***
## PubDate.day.minutes.poly.1
## H.sum.TfIdf
## S.sum.TfIdf
## A.sum.TfIdf
## `PubDate.hour.fctr(7.67,15.3]`
## `PubDate.hour.fctr(15.3,23]`
## H.npnct19.log ***
## PubDate.wkend
## H.P.recap.colon
## H.P.quandary
## PubDate.day.minutes.poly.4
## PubDate.day.minutes.poly.2 **
## H.P.no.comment.colon
## H.P.facts.figures
## PubDate.last10.log
## PubDate.last1.log
## A.T.make
## S.T.make
## H.T.get
## H.npnct06.log
## S.npnct01.log
## A.T.can
## H.npnct16.log
## S.T.can
## PubDate.day.minutes.poly.3
## H.T.ebola .
## H.npnct01.log
## A.T.said
## S.T.said
## H.T.make
## H.npnct11.log .
## A.T.one
## S.T.one
## H.P.s.notebook
## H.T.take
## S.npnct16.log
## A.T.presid
## S.T.presid
## PubDate.last100.log
## .rnorm
## H.npnct05.log
## H.P.friday.night.music *
## H.T.say
## H.T.obama
## H.T.bank
## `PubDate.date.fctr(7,13]`
## `PubDate.date.fctr(13,19]`
## `PubDate.date.fctr(19,25]`
## `PubDate.date.fctr(25,31]`
## `PubDate.second.fctr(14.8,29.5]`
## `PubDate.second.fctr(29.5,44.2]`
## `PubDate.second.fctr(44.2,59.1]`
## H.npnct07.log
## S.npnct07.log
## S.npnct03.log
## A.npnct18.log
## H.npnct12.log
## H.T.word .
## H.T.big
## A.npnct02.log
## A.npnct17.log
## S.P.year.colon
## S.T.obama
## A.T.obama
## S.npnct20.log
## H.npnct02.log
## H.T.test
## H.P.on.this.day
## S.P.first.draft
## S.T.take
## A.T.take
## S.npnct06.log
## A.npnct14.log
## S.T.time
## A.T.time
## H.T.newyorktim
## H.T.deal
## S.T.new
## A.T.new
## H.T.billion
## S.P.metropolitan.diary.colon *
## H.T.polit
## H.P.verbatim.colon
## H.T.china
## H.T.art
## `PubDate.minute.fctr(14.8,29.5]`
## `PubDate.minute.fctr(29.5,44.2]`
## `PubDate.minute.fctr(44.2,59.1]`
## H.T.read
## S.npnct12.log
## H.P.today.in.politic
## A.T.year
## S.T.year
## H.P.what.we.are
## A.T.will
## S.T.will
## A.T.appear
## S.T.appear
## PubDate.wkday.fctr1
## PubDate.wkday.fctr2 .
## PubDate.wkday.fctr3
## PubDate.wkday.fctr4
## PubDate.wkday.fctr5
## PubDate.wkday.fctr6 .
## H.T.pictur
## H.T.new
## A.T.senat
## S.T.senat
## S.T.show
## A.T.show
## H.P.today.in.smallbusiness
## S.T.day
## A.T.day
## S.npnct28.log
## A.npnct28.log
## H.P.daily.clip.report
## H.T.clip
## A.T.first
## H.T.news
## S.T.first
## H.T.first
## H.T.X2014
## A.T.newyork
## S.T.newyork
## A.T.report
## A.T.compani
## S.T.report
## S.T.compani
## A.T.word
## S.T.word
## H.T.morn
## H.T.busi
## A.T.newyorktim
## S.T.newyorktim
## A.T.share
## S.T.share
## H.npnct04.log .
## A.T.articl
## S.T.articl
## H.T.newyork
## PubDate.day.minutes.poly.5
## H.T.today .
## H.T.springsumm
## H.T.day .
## H.npnct14.log
## A.T.diari *
## S.T.diari
## H.T.report
## S.npnct04.log
## H.T.daili
## H.T.X2015
## A.T.herald
## S.T.herald
## H.T.week
## A.T.photo
## S.T.photo
## A.T.intern
## S.T.intern
## A.T.tribun
## S.T.tribun
## S.P.fashion.week
## A.T.archiv
## S.T.archiv
## H.P.fashion.week
## H.P.year.colon
## H.T.fashion
## A.T.fashion
## S.T.fashion
## A.T.week
## S.T.week
## H.nstopwrds.log
## H.npnct28.log
## S.npnct11.log
## S.nstopwrds.log
## A.nstopwrds.log
## H.ndgts.log
## S.ndgts.log
## H.nwrds.log
## H.nchrs.log *
## S.nwrds.log
## A.nwrds.log
## S.nchrs.log
## `myCategory.fctr##:.clusterid.fctr2`
## `myCategory.fctr#Multimedia#:.clusterid.fctr2`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2`
## `myCategory.fctrForeign#World#:.clusterid.fctr2`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2`
## `myCategory.fctrmyOther:.clusterid.fctr2`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2`
## `myCategory.fctrScience#Health#:.clusterid.fctr2`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2`
## `myCategory.fctrTStyle##:.clusterid.fctr2`
## `myCategory.fctr##:.clusterid.fctr3`
## `myCategory.fctr#Multimedia#:.clusterid.fctr3`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3`
## `myCategory.fctrForeign#World#:.clusterid.fctr3`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3`
## `myCategory.fctrmyOther:.clusterid.fctr3`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3`
## `myCategory.fctrScience#Health#:.clusterid.fctr3`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3`
## `myCategory.fctrTStyle##:.clusterid.fctr3`
## `myCategory.fctr##:.clusterid.fctr4`
## `myCategory.fctr#Multimedia#:.clusterid.fctr4`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` .
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4`
## `myCategory.fctrForeign#World#:.clusterid.fctr4`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4`
## `myCategory.fctrmyOther:.clusterid.fctr4`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4`
## `myCategory.fctrScience#Health#:.clusterid.fctr4`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4`
## `myCategory.fctrTStyle##:.clusterid.fctr4`
## `myCategory.fctr##:.clusterid.fctr5`
## `myCategory.fctr#Multimedia#:.clusterid.fctr5`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5`
## `myCategory.fctrForeign#World#:.clusterid.fctr5`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5`
## `myCategory.fctrmyOther:.clusterid.fctr5`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5`
## `myCategory.fctrScience#Health#:.clusterid.fctr5`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5`
## `myCategory.fctrTStyle##:.clusterid.fctr5`
## `myCategory.fctr##:.clusterid.fctr6`
## `myCategory.fctr#Multimedia#:.clusterid.fctr6`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6`
## `myCategory.fctrForeign#World#:.clusterid.fctr6`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6`
## `myCategory.fctrmyOther:.clusterid.fctr6`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6`
## `myCategory.fctrScience#Health#:.clusterid.fctr6`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6`
## `myCategory.fctrTStyle##:.clusterid.fctr6`
## `myCategory.fctr##:.clusterid.fctr7`
## `myCategory.fctr#Multimedia#:.clusterid.fctr7`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7`
## `myCategory.fctrForeign#World#:.clusterid.fctr7`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7`
## `myCategory.fctrmyOther:.clusterid.fctr7`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7`
## `myCategory.fctrScience#Health#:.clusterid.fctr7`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7`
## `myCategory.fctrTStyle##:.clusterid.fctr7`
## `myCategory.fctr##:.clusterid.fctr8`
## `myCategory.fctr#Multimedia#:.clusterid.fctr8`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8`
## `myCategory.fctrForeign#World#:.clusterid.fctr8`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8`
## `myCategory.fctrmyOther:.clusterid.fctr8`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` .
## `myCategory.fctrScience#Health#:.clusterid.fctr8`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8`
## `myCategory.fctrTStyle##:.clusterid.fctr8`
## `myCategory.fctr##:.clusterid.fctr9`
## `myCategory.fctr#Multimedia#:.clusterid.fctr9`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9`
## `myCategory.fctrForeign#World#:.clusterid.fctr9`
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9`
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## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9`
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## `myCategory.fctrStyles##Fashion:.clusterid.fctr9`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9`
## `myCategory.fctrTStyle##:.clusterid.fctr9`
## `myCategory.fctr##:.clusterid.fctr10`
## `myCategory.fctr#Multimedia#:.clusterid.fctr10`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10`
## `myCategory.fctrForeign#World#:.clusterid.fctr10`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10`
## `myCategory.fctrmyOther:.clusterid.fctr10`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10`
## `myCategory.fctrScience#Health#:.clusterid.fctr10`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10`
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## `myCategory.fctr##:.clusterid.fctr11`
## `myCategory.fctr#Multimedia#:.clusterid.fctr11`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11`
## `myCategory.fctrForeign#World#:.clusterid.fctr11`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11`
## `myCategory.fctrmyOther:.clusterid.fctr11`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11`
## `myCategory.fctrScience#Health#:.clusterid.fctr11`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11`
## `myCategory.fctrTStyle##:.clusterid.fctr11`
## `myCategory.fctr##:.clusterid.fctr12`
## `myCategory.fctr#Multimedia#:.clusterid.fctr12`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12`
## `myCategory.fctrForeign#World#:.clusterid.fctr12`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12`
## `myCategory.fctrmyOther:.clusterid.fctr12`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12`
## `myCategory.fctrScience#Health#:.clusterid.fctr12`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12`
## `myCategory.fctrTStyle##:.clusterid.fctr12`
## `myCategory.fctr##:.clusterid.fctr13`
## `myCategory.fctr#Multimedia#:.clusterid.fctr13`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13`
## `myCategory.fctrForeign#World#:.clusterid.fctr13`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13`
## `myCategory.fctrmyOther:.clusterid.fctr13`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13`
## `myCategory.fctrScience#Health#:.clusterid.fctr13`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13`
## `myCategory.fctrTStyle##:.clusterid.fctr13`
## `myCategory.fctr##:.clusterid.fctr14`
## `myCategory.fctr#Multimedia#:.clusterid.fctr14`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14`
## `myCategory.fctrForeign#World#:.clusterid.fctr14`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14`
## `myCategory.fctrmyOther:.clusterid.fctr14`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14`
## `myCategory.fctrScience#Health#:.clusterid.fctr14`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14`
## `myCategory.fctrTStyle##:.clusterid.fctr14`
## `myCategory.fctr##:.clusterid.fctr15`
## `myCategory.fctr#Multimedia#:.clusterid.fctr15`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15`
## `myCategory.fctrForeign#World#:.clusterid.fctr15`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15`
## `myCategory.fctrmyOther:.clusterid.fctr15`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15`
## `myCategory.fctrScience#Health#:.clusterid.fctr15`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15`
## `myCategory.fctrTStyle##:.clusterid.fctr15`
## `myCategory.fctr##:.clusterid.fctr16`
## `myCategory.fctr#Multimedia#:.clusterid.fctr16`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16`
## `myCategory.fctrForeign#World#:.clusterid.fctr16`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16`
## `myCategory.fctrmyOther:.clusterid.fctr16`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16`
## `myCategory.fctrScience#Health#:.clusterid.fctr16`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16`
## `myCategory.fctrTStyle##:.clusterid.fctr16`
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## `myCategory.fctr#Multimedia#:.clusterid.fctr17`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17`
## `myCategory.fctrForeign#World#:.clusterid.fctr17`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17`
## `myCategory.fctrmyOther:.clusterid.fctr17`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17`
## `myCategory.fctrScience#Health#:.clusterid.fctr17`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17`
## `myCategory.fctrTStyle##:.clusterid.fctr17`
## `myCategory.fctr##:.clusterid.fctr18`
## `myCategory.fctr#Multimedia#:.clusterid.fctr18`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr18`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr18`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr18`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr18`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr18`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr18`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr18`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr18`
## `myCategory.fctrForeign#World#:.clusterid.fctr18`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18`
## `myCategory.fctrmyOther:.clusterid.fctr18`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18`
## `myCategory.fctrScience#Health#:.clusterid.fctr18`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18`
## `myCategory.fctrTStyle##:.clusterid.fctr18`
## `myCategory.fctr##:.clusterid.fctr19`
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## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19`
## `myCategory.fctrForeign#World#:.clusterid.fctr19`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19`
## `myCategory.fctrmyOther:.clusterid.fctr19`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19`
## `myCategory.fctrScience#Health#:.clusterid.fctr19`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19`
## `myCategory.fctrTStyle##:.clusterid.fctr19`
## `myCategory.fctr##:.clusterid.fctr20`
## `myCategory.fctr#Multimedia#:.clusterid.fctr20`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20`
## `myCategory.fctrForeign#World#:.clusterid.fctr20`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20`
## `myCategory.fctrmyOther:.clusterid.fctr20`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20`
## `myCategory.fctrScience#Health#:.clusterid.fctr20`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20`
## `myCategory.fctrTStyle##:.clusterid.fctr20`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 1719.7 on 4213 degrees of freedom
## AIC: 2243.7
##
## Number of Fisher Scoring iterations: 19
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2867534
## 2 0.1 0.6672995
## 3 0.2 0.7400783
## 4 0.3 0.7640867
## 5 0.4 0.7553763
## 6 0.5 0.7406877
## 7 0.6 0.7216339
## 8 0.7 0.6672104
## 9 0.8 0.6001781
## 10 0.9 0.4383282
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.All.X.2.glm.N
## 1 N 3477
## 2 Y 132
## Popular.fctr.predict.All.X.2.glm.Y
## 1 249
## 2 617
## Prediction
## Reference N Y
## N 3477 249
## Y 132 617
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.148603e-01 7.124762e-01 9.062982e-01 9.228764e-01 8.326257e-01
## AccuracyPValue McnemarPValue
## 9.523435e-58 2.800916e-09
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2865473
## 2 0.1 0.6088710
## 3 0.2 0.6731001
## 4 0.3 0.7095047
## 5 0.4 0.7225434
## 6 0.5 0.7085890
## 7 0.6 0.6893204
## 8 0.7 0.6423611
## 9 0.8 0.5581395
## 10 0.9 0.3809524
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## Popular.fctr Popular.fctr.predict.All.X.2.glm.N
## 1 N 1615
## 2 Y 94
## Popular.fctr.predict.All.X.2.glm.Y
## 1 98
## 2 250
## Prediction
## Reference N Y
## N 1615 98
## Y 94 250
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.066602e-01 6.664381e-01 8.932593e-01 9.188884e-01 8.327662e-01
## AccuracyPValue McnemarPValue
## 3.600992e-22 8.285931e-01
## model_id model_method
## 1 All.X.2.glm glm
## feats
## 1 WordCount.log, myCategory.fctr, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr, H.npnct19.log, PubDate.wkend, H.P.recap.colon, H.P.quandary, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, H.P.no.comment.colon, H.P.facts.figures, PubDate.last10.log, PubDate.last1.log, A.T.make, S.T.make, H.T.get, H.npnct06.log, S.npnct01.log, A.T.can, H.npnct16.log, S.T.can, PubDate.day.minutes.poly.3, H.T.ebola, H.npnct01.log, A.T.said, S.T.said, H.T.make, H.npnct11.log, A.T.one, S.T.one, H.P.s.notebook, H.T.take, S.npnct16.log, A.T.presid, S.T.presid, PubDate.last100.log, .rnorm, H.npnct05.log, H.P.friday.night.music, H.T.say, H.T.obama, H.T.bank, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, H.T.word, H.T.big, A.npnct02.log, A.npnct17.log, S.P.year.colon, S.T.obama, A.T.obama, S.npnct20.log, H.npnct02.log, H.T.test, H.P.on.this.day, S.P.first.draft, S.T.take, A.T.take, S.npnct06.log, A.npnct14.log, S.T.time, A.T.time, H.T.newyorktim, H.T.deal, S.T.new, A.T.new, H.T.billion, S.P.metropolitan.diary.colon, H.T.polit, H.P.verbatim.colon, H.T.china, H.T.art, PubDate.minute.fctr, H.T.read, S.npnct12.log, H.P.today.in.politic, A.T.year, S.T.year, H.P.what.we.are, A.T.will, S.T.will, A.T.appear, S.T.appear, PubDate.wkday.fctr, H.T.pictur, H.T.new, A.T.senat, S.T.senat, S.T.show, A.T.show, H.P.today.in.smallbusiness, S.T.day, A.T.day, S.npnct28.log, A.npnct28.log, H.P.daily.clip.report, H.T.clip, A.T.first, H.T.news, S.T.first, H.T.first, H.T.X2014, A.T.newyork, S.T.newyork, A.T.report, A.T.compani, S.T.report, S.T.compani, A.T.word, S.T.word, H.T.morn, H.T.busi, A.T.newyorktim, S.T.newyorktim, A.T.share, S.T.share, H.npnct04.log, A.T.articl, S.T.articl, H.T.newyork, PubDate.day.minutes.poly.5, H.T.today, H.T.springsumm, H.T.day, H.npnct14.log, A.T.diari, S.T.diari, H.T.report, S.npnct04.log, H.T.daili, H.T.X2015, A.T.herald, S.T.herald, H.T.week, A.T.photo, S.T.photo, A.T.intern, S.T.intern, A.T.tribun, S.T.tribun, S.P.fashion.week, A.T.archiv, S.T.archiv, H.P.fashion.week, H.P.year.colon, H.T.fashion, A.T.fashion, S.T.fashion, A.T.week, S.T.week, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, S.nchrs.log, myCategory.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 171.251 77.371
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9573387 0.3 0.7640867 0.8929616
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9062982 0.9228764 0.605434 0.9104268
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7225434 0.9066602
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.8932593 0.9188884 0.6664381 2243.694
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.007840729 0.0231965#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#varImp(glb_models_lst[["All.X.2.glm"]])
#glb_feats_df[grepl("nwrds.unq", glb_feats_df$id), ]
#print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id)); print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)], glb_OOBobs_df[, glb_rsp_var])$table))
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id model_method
## MFO.myMFO_classfr MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.glm Max.cor.Y.glm glm
## Max.cor.Y.TmSrs.poly.glm Max.cor.Y.TmSrs.poly.glm glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm glm
## Low.cor.X.glm Low.cor.X.glm glm
## All.X.glm All.X.glm glm
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## Rank9.Bayes.glm.glm Rank9.Bayes.glm.glm glm
## All.X.2.glm All.X.2.glm glm
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart A.nuppr.log, WordCount.log
## Max.cor.Y.cv.0.cp.0.rpart A.nuppr.log, WordCount.log
## Max.cor.Y.rpart A.nuppr.log, WordCount.log
## Max.cor.Y.glm A.nuppr.log, WordCount.log
## Max.cor.Y.TmSrs.poly.glm A.nuppr.log, WordCount.log, PubDate.day.minutes.poly.1, PubDate.day.minutes.poly.2, PubDate.day.minutes.poly.3, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.5
## Interact.High.cor.Y.glm A.nuppr.log, WordCount.log, A.nuppr.log:A.ratio.sum.TfIdf.nwrds, A.nuppr.log:S.sum.TfIdf, A.nuppr.log:PubDate.day.minutes.poly.1, A.nuppr.log:A.ratio.nstopwrds.nwrds, A.nuppr.log:S.npnct19.log, A.nuppr.log:A.T.make, A.nuppr.log:A.npnct01.log, A.nuppr.log:H.npnct06.log, A.nuppr.log:A.T.can, A.nuppr.log:S.npnct21.log, A.nuppr.log:A.T.said, A.nuppr.log:A.npnct21.log, A.nuppr.log:A.T.one, A.nuppr.log:A.npnct07.log, A.nuppr.log:A.npnct03.log, A.nuppr.log:A.P.http, A.nuppr.log:A.npnct02.log, A.nuppr.log:A.T.appear, A.nuppr.log:A.npnct17.log, A.nuppr.log:A.npnct14.log, A.nuppr.log:A.P.year.colon, A.nuppr.log:A.T.obama, A.nuppr.log:A.npnct20.log, A.nuppr.log:A.P.first.draft, A.nuppr.log:A.T.take, A.nuppr.log:A.npnct06.log, A.nuppr.log:A.T.time, A.nuppr.log:A.T.new, A.nuppr.log:H.npnct04.log, A.nuppr.log:A.P.metropolitan.diary.colon, A.nuppr.log:H.P.today.in.politic, A.nuppr.log:H.P.what.we.are, A.nuppr.log:A.npnct12.log, A.nuppr.log:H.T.today, A.nuppr.log:S.T.year, A.nuppr.log:S.T.will, A.nuppr.log:S.T.senat, A.nuppr.log:A.T.show, A.nuppr.log:A.T.day, A.nuppr.log:H.T.first, A.nuppr.log:A.npnct28.log, A.nuppr.log:H.T.morn, A.nuppr.log:H.T.daili, A.nuppr.log:A.P.daily.clip.report, A.nuppr.log:S.T.first, A.nuppr.log:S.T.newyork, A.nuppr.log:S.T.report, A.nuppr.log:S.T.compani, A.nuppr.log:S.T.word, A.nuppr.log:S.T.newyorktim, A.nuppr.log:S.npnct13.log, A.nuppr.log:A.T.share, A.nuppr.log:S.T.articl, A.nuppr.log:H.npnct14.log, A.nuppr.log:H.T.X2015, A.nuppr.log:A.T.diari, A.nuppr.log:A.npnct04.log, A.nuppr.log:S.T.fashion, A.nuppr.log:A.T.tribun, A.nuppr.log:A.T.herald, A.nuppr.log:A.npnct15.log, A.nuppr.log:S.T.photo, A.nuppr.log:S.T.intern, A.nuppr.log:A.T.archiv, A.nuppr.log:A.P.fashion.week, A.nuppr.log:H.P.year.colon, A.nuppr.log:H.T.fashion, A.nuppr.log:S.T.week, A.nuppr.log:A.npnct11.log, A.nuppr.log:A.nstopwrds.log, A.nuppr.log:A.ndgts.log, A.nuppr.log:H.nwrds.unq.log, A.nuppr.log:H.nchrs.log, A.nuppr.log:A.nwrds.log, A.nuppr.log:S.nchrs.log, A.nuppr.log:S.nwrds.unq.log, A.nuppr.log:A.nuppr.log
## Low.cor.X.glm WordCount.log, A.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, H.npnct19.log, A.ratio.nstopwrds.nwrds, PubDate.wkend, H.P.recap.colon, H.P.quandary, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, H.P.no.comment.colon, S.npnct19.log, H.P.facts.figures, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.P.readers.respond, A.T.make, H.ratio.nstopwrds.nwrds, H.T.get, H.npnct06.log, A.npnct01.log, A.T.can, PubDate.day.minutes.poly.3, H.T.ebola, H.npnct01.log, A.T.said, H.T.make, H.npnct11.log, A.T.one, H.P.s.notebook, H.T.take, A.npnct16.log, S.npnct16.log, A.T.presid, S.T.presid, S.npnct08.log, A.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, H.P.friday.night.music, H.T.say, H.T.obama, H.T.bank, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, H.npnct12.log, A.npnct03.log, H.T.big, A.P.year.colon, A.T.obama, A.npnct20.log, H.npnct02.log, H.T.test, S.npnct14.log, A.P.first.draft, H.P.on.this.day, A.T.take, A.npnct06.log, A.npnct14.log, A.T.time, H.T.newyorktim, H.npnct13.log, H.T.deal, A.T.new, A.P.metropolitan.diary.colon, H.P.verbatim.colon, H.T.china, H.T.art, PubDate.minute.fctr, S.T.year, A.npnct12.log, H.P.what.we.are, S.T.will, A.T.appear, PubDate.wkday.fctr, H.T.pictur, H.T.new, S.T.senat, A.T.show, H.P.today.in.smallbusiness, A.T.day, H.T.news, S.T.first, H.T.first, H.T.X2014, S.T.newyork, S.T.report, S.T.compani, S.T.word, H.T.morn, H.T.busi, S.T.newyorktim, A.T.share, H.npnct04.log, S.npnct13.log, S.T.articl, H.T.newyork, PubDate.day.minutes.poly.5, H.T.today, H.T.day, A.npnct04.log, H.T.daili, H.T.week, S.T.photo, A.npnct15.log, H.P.year.colon, H.T.fashion, H.npnct15.log, S.T.fashion, S.T.week, H.nstopwrds.log, H.npnct28.log, A.npnct11.log, H.ndgts.log, A.ndgts.log, H.nwrds.unq.log, A.nuppr.log, myCategory.fctr:.clusterid.fctr
## All.X.glm WordCount.log, A.ratio.sum.TfIdf.nwrds, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, H.P.recap.colon, H.P.quandary, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, H.P.no.comment.colon, S.npnct19.log, H.P.facts.figures, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.P.readers.respond, A.T.make, S.T.make, H.ratio.nstopwrds.nwrds, H.T.get, H.npnct06.log, S.npnct01.log, A.T.can, H.npnct16.log, S.T.can, PubDate.day.minutes.poly.3, H.T.ebola, H.npnct01.log, A.T.said, S.T.said, H.T.make, H.npnct11.log, A.T.one, S.T.one, H.P.s.notebook, H.T.take, S.npnct16.log, A.T.presid, S.T.presid, S.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, H.P.friday.night.music, H.T.say, H.T.obama, H.T.bank, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, H.T.word, H.T.big, A.npnct02.log, A.npnct17.log, S.P.year.colon, S.T.obama, A.T.obama, S.npnct20.log, H.npnct02.log, H.T.test, H.P.on.this.day, S.P.first.draft, S.T.take, A.T.take, S.npnct06.log, A.npnct14.log, S.T.time, A.T.time, H.T.newyorktim, H.npnct13.log, H.T.deal, S.T.new, A.T.new, H.T.billion, S.P.metropolitan.diary.colon, H.T.polit, H.P.verbatim.colon, H.T.china, H.T.art, PubDate.minute.fctr, H.T.read, S.npnct12.log, H.P.today.in.politic, A.T.year, S.T.year, H.P.what.we.are, A.T.will, S.T.will, A.T.appear, S.T.appear, PubDate.wkday.fctr, H.T.pictur, H.T.new, A.T.senat, S.T.senat, S.T.show, A.T.show, H.P.today.in.smallbusiness, S.T.day, A.T.day, S.npnct28.log, A.npnct28.log, H.P.daily.clip.report, H.T.clip, A.T.first, H.T.news, S.T.first, H.T.first, H.T.X2014, A.T.newyork, S.T.newyork, A.T.report, A.T.compani, S.T.report, S.T.compani, A.T.word, S.T.word, H.T.morn, H.T.busi, A.T.newyorktim, S.T.newyorktim, A.npnct13.log, A.T.share, S.T.share, H.npnct04.log, S.npnct13.log, A.T.articl, S.T.articl, H.T.newyork, PubDate.day.minutes.poly.5, H.T.today, H.T.springsumm, H.T.day, H.npnct14.log, A.T.diari, S.T.diari, H.T.report, S.npnct04.log, H.T.daili, H.T.X2015, A.T.herald, S.T.herald, S.npnct15.log, H.T.week, A.T.photo, S.T.photo, A.T.intern, S.T.intern, A.T.tribun, S.T.tribun, S.P.fashion.week, A.T.archiv, S.T.archiv, H.P.fashion.week, H.P.year.colon, H.T.fashion, H.npnct15.log, A.T.fashion, S.T.fashion, A.T.week, S.T.week, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr
## All.X.no.rnorm.rpart WordCount.log, A.ratio.sum.TfIdf.nwrds, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, H.P.recap.colon, H.P.quandary, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, H.P.no.comment.colon, S.npnct19.log, H.P.facts.figures, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.P.readers.respond, A.T.make, S.T.make, H.ratio.nstopwrds.nwrds, H.T.get, H.npnct06.log, S.npnct01.log, A.T.can, H.npnct16.log, S.T.can, PubDate.day.minutes.poly.3, H.T.ebola, H.npnct01.log, A.T.said, S.T.said, H.T.make, H.npnct11.log, A.T.one, S.T.one, H.P.s.notebook, H.T.take, S.npnct16.log, A.T.presid, S.T.presid, S.npnct08.log, PubDate.last100.log, H.npnct05.log, H.P.friday.night.music, H.T.say, H.T.obama, H.T.bank, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, H.T.word, H.T.big, A.npnct02.log, A.npnct17.log, S.P.year.colon, S.T.obama, A.T.obama, S.npnct20.log, H.npnct02.log, H.T.test, H.P.on.this.day, S.P.first.draft, S.T.take, A.T.take, S.npnct06.log, A.npnct14.log, S.T.time, A.T.time, H.T.newyorktim, H.npnct13.log, H.T.deal, S.T.new, A.T.new, H.T.billion, S.P.metropolitan.diary.colon, H.T.polit, H.P.verbatim.colon, H.T.china, H.T.art, PubDate.minute.fctr, H.T.read, S.npnct12.log, H.P.today.in.politic, A.T.year, S.T.year, H.P.what.we.are, A.T.will, S.T.will, A.T.appear, S.T.appear, PubDate.wkday.fctr, H.T.pictur, H.T.new, A.T.senat, S.T.senat, S.T.show, A.T.show, H.P.today.in.smallbusiness, S.T.day, A.T.day, S.npnct28.log, A.npnct28.log, H.P.daily.clip.report, H.T.clip, A.T.first, H.T.news, S.T.first, H.T.first, H.T.X2014, A.T.newyork, S.T.newyork, A.T.report, A.T.compani, S.T.report, S.T.compani, A.T.word, S.T.word, H.T.morn, H.T.busi, A.T.newyorktim, S.T.newyorktim, A.npnct13.log, A.T.share, S.T.share, H.npnct04.log, S.npnct13.log, A.T.articl, S.T.articl, H.T.newyork, PubDate.day.minutes.poly.5, H.T.today, H.T.springsumm, H.T.day, H.npnct14.log, A.T.diari, S.T.diari, H.T.report, S.npnct04.log, H.T.daili, H.T.X2015, A.T.herald, S.T.herald, S.npnct15.log, H.T.week, A.T.photo, S.T.photo, A.T.intern, S.T.intern, A.T.tribun, S.T.tribun, S.P.fashion.week, A.T.archiv, S.T.archiv, H.P.fashion.week, H.P.year.colon, H.T.fashion, H.npnct15.log, A.T.fashion, S.T.fashion, A.T.week, S.T.week, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr
## Rank9.Bayes.glm.glm myCategory.fctr*WordCount.log, myCategory.fctr:.clusterid.fctr, myCategory.fctr*H.npnct19.log, myCategory.fctr:PubDate.day.minutes.poly.1, myCategory.fctr:PubDate.day.minutes.poly.2, myCategory.fctr:PubDate.day.minutes.poly.3, myCategory.fctr:PubDate.day.minutes.poly.4, myCategory.fctr:PubDate.day.minutes.poly.5
## All.X.2.glm WordCount.log, myCategory.fctr, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr, H.npnct19.log, PubDate.wkend, H.P.recap.colon, H.P.quandary, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, H.P.no.comment.colon, H.P.facts.figures, PubDate.last10.log, PubDate.last1.log, A.T.make, S.T.make, H.T.get, H.npnct06.log, S.npnct01.log, A.T.can, H.npnct16.log, S.T.can, PubDate.day.minutes.poly.3, H.T.ebola, H.npnct01.log, A.T.said, S.T.said, H.T.make, H.npnct11.log, A.T.one, S.T.one, H.P.s.notebook, H.T.take, S.npnct16.log, A.T.presid, S.T.presid, PubDate.last100.log, .rnorm, H.npnct05.log, H.P.friday.night.music, H.T.say, H.T.obama, H.T.bank, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, H.T.word, H.T.big, A.npnct02.log, A.npnct17.log, S.P.year.colon, S.T.obama, A.T.obama, S.npnct20.log, H.npnct02.log, H.T.test, H.P.on.this.day, S.P.first.draft, S.T.take, A.T.take, S.npnct06.log, A.npnct14.log, S.T.time, A.T.time, H.T.newyorktim, H.T.deal, S.T.new, A.T.new, H.T.billion, S.P.metropolitan.diary.colon, H.T.polit, H.P.verbatim.colon, H.T.china, H.T.art, PubDate.minute.fctr, H.T.read, S.npnct12.log, H.P.today.in.politic, A.T.year, S.T.year, H.P.what.we.are, A.T.will, S.T.will, A.T.appear, S.T.appear, PubDate.wkday.fctr, H.T.pictur, H.T.new, A.T.senat, S.T.senat, S.T.show, A.T.show, H.P.today.in.smallbusiness, S.T.day, A.T.day, S.npnct28.log, A.npnct28.log, H.P.daily.clip.report, H.T.clip, A.T.first, H.T.news, S.T.first, H.T.first, H.T.X2014, A.T.newyork, S.T.newyork, A.T.report, A.T.compani, S.T.report, S.T.compani, A.T.word, S.T.word, H.T.morn, H.T.busi, A.T.newyorktim, S.T.newyorktim, A.T.share, S.T.share, H.npnct04.log, A.T.articl, S.T.articl, H.T.newyork, PubDate.day.minutes.poly.5, H.T.today, H.T.springsumm, H.T.day, H.npnct14.log, A.T.diari, S.T.diari, H.T.report, S.npnct04.log, H.T.daili, H.T.X2015, A.T.herald, S.T.herald, H.T.week, A.T.photo, S.T.photo, A.T.intern, S.T.intern, A.T.tribun, S.T.tribun, S.P.fashion.week, A.T.archiv, S.T.archiv, H.P.fashion.week, H.P.year.colon, H.T.fashion, A.T.fashion, S.T.fashion, A.T.week, S.T.week, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, S.nchrs.log, myCategory.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything
## MFO.myMFO_classfr 0 0.940
## Random.myrandom_classfr 0 0.362
## Max.cor.Y.cv.0.rpart 0 0.882
## Max.cor.Y.cv.0.cp.0.rpart 0 0.669
## Max.cor.Y.rpart 3 1.461
## Max.cor.Y.glm 1 1.339
## Max.cor.Y.TmSrs.poly.glm 1 1.554
## Interact.High.cor.Y.glm 1 4.557
## Low.cor.X.glm 1 147.979
## All.X.glm 1 182.782
## All.X.no.rnorm.rpart 3 28.283
## Rank9.Bayes.glm.glm 1 246.988
## All.X.2.glm 1 171.251
## min.elapsedtime.final max.auc.fit
## MFO.myMFO_classfr 0.003 0.5000000
## Random.myrandom_classfr 0.001 0.5072166
## Max.cor.Y.cv.0.rpart 0.085 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.079 0.8239196
## Max.cor.Y.rpart 0.087 0.5000000
## Max.cor.Y.glm 0.094 0.7879886
## Max.cor.Y.TmSrs.poly.glm 0.188 0.8146498
## Interact.High.cor.Y.glm 1.862 0.8805672
## Low.cor.X.glm 68.071 0.9609008
## All.X.glm 86.095 0.9637398
## All.X.no.rnorm.rpart 6.421 0.7277461
## Rank9.Bayes.glm.glm 106.617 0.9592523
## All.X.2.glm 77.371 0.9573387
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.1 0.2867534
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.3 0.5137615
## Max.cor.Y.rpart 0.5 0.0000000
## Max.cor.Y.glm 0.2 0.4581986
## Max.cor.Y.TmSrs.poly.glm 0.3 0.5006289
## Interact.High.cor.Y.glm 0.3 0.5912409
## Low.cor.X.glm 0.3 0.7662740
## All.X.glm 0.3 0.7797030
## All.X.no.rnorm.rpart 0.7 0.5978351
## Rank9.Bayes.glm.glm 0.3 0.7565589
## All.X.2.glm 0.3 0.7640867
## max.Accuracy.fit max.AccuracyLower.fit
## MFO.myMFO_classfr 0.8326257 0.8213602
## Random.myrandom_classfr 0.1673743 0.1565447
## Max.cor.Y.cv.0.rpart 0.8326257 0.8213602
## Max.cor.Y.cv.0.cp.0.rpart 0.8341899 0.8229644
## Max.cor.Y.rpart 0.8232385 0.8213602
## Max.cor.Y.glm 0.8283787 0.7247275
## Max.cor.Y.TmSrs.poly.glm 0.8373175 0.8110556
## Interact.High.cor.Y.glm 0.8643586 0.8390261
## Low.cor.X.glm 0.9032400 0.9072305
## All.X.glm 0.8931847 0.9121295
## All.X.no.rnorm.rpart 0.8992181 0.8826068
## Rank9.Bayes.glm.glm 0.8884929 0.9021062
## All.X.2.glm 0.8929616 0.9062982
## max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## MFO.myMFO_classfr 0.8434553 0.0000000 0.5000000
## Random.myrandom_classfr 0.1786398 0.0000000 0.4877001
## Max.cor.Y.cv.0.rpart 0.8434553 0.0000000 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8449775 0.4138559 0.7482444
## Max.cor.Y.rpart 0.8434553 0.1140417 0.5000000
## Max.cor.Y.glm 0.7507156 0.1138834 0.7836398
## Max.cor.Y.TmSrs.poly.glm 0.8336614 0.2130159 0.8083559
## Interact.High.cor.Y.glm 0.8601799 0.4117899 0.8577584
## Low.cor.X.glm 0.9237306 0.6385029 0.9169373
## All.X.glm 0.9282106 0.6089725 0.9084175
## All.X.no.rnorm.rpart 0.9010121 0.5798643 0.7084504
## Rank9.Bayes.glm.glm 0.9190291 0.6023604 0.9326203
## All.X.2.glm 0.9228764 0.6054340 0.9104268
## opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.1 0.2865473
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.2 0.4240506
## Max.cor.Y.rpart 0.5 0.0000000
## Max.cor.Y.glm 0.2 0.4413240
## Max.cor.Y.TmSrs.poly.glm 0.2 0.5005192
## Interact.High.cor.Y.glm 0.3 0.5565670
## Low.cor.X.glm 0.4 0.7230321
## All.X.glm 0.4 0.7124464
## All.X.no.rnorm.rpart 0.7 0.5650558
## Rank9.Bayes.glm.glm 0.5 0.7445255
## All.X.2.glm 0.4 0.7225434
## max.Accuracy.OOB max.AccuracyLower.OOB
## MFO.myMFO_classfr 0.8327662 0.8159247
## Random.myrandom_classfr 0.1672338 0.1513467
## Max.cor.Y.cv.0.rpart 0.8327662 0.8159247
## Max.cor.Y.cv.0.cp.0.rpart 0.7345649 0.7149143
## Max.cor.Y.rpart 0.8327662 0.8159247
## Max.cor.Y.glm 0.7292173 0.7094536
## Max.cor.Y.TmSrs.poly.glm 0.7661643 0.7472579
## Interact.High.cor.Y.glm 0.8342246 0.8174373
## Low.cor.X.glm 0.9076325 0.8942890
## All.X.glm 0.9022849 0.8886309
## All.X.no.rnorm.rpart 0.8862421 0.8717239
## Rank9.Bayes.glm.glm 0.9149246 0.9020252
## All.X.2.glm 0.9066602 0.8932593
## max.AccuracyUpper.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.8486533 0.0000000
## Random.myrandom_classfr 0.1840753 0.0000000
## Max.cor.Y.cv.0.rpart 0.8486533 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.7535448 0.2680784
## Max.cor.Y.rpart 0.8486533 0.0000000
## Max.cor.Y.glm 0.7483257 0.2846066
## Max.cor.Y.TmSrs.poly.glm 0.7843092 0.3637267
## Interact.High.cor.Y.glm 0.8500533 0.4560113
## Low.cor.X.glm 0.9198004 0.6676067
## All.X.glm 0.9147795 0.6536059
## All.X.no.rnorm.rpart 0.8996488 0.5054039
## Rank9.Bayes.glm.glm 0.9266265 0.6934914
## All.X.2.glm 0.9188884 0.6664381
## max.AccuracySD.fit max.KappaSD.fit min.aic.fit
## MFO.myMFO_classfr NA NA NA
## Random.myrandom_classfr NA NA NA
## Max.cor.Y.cv.0.rpart NA NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA NA
## Max.cor.Y.rpart 0.006649759 0.03033634 NA
## Max.cor.Y.glm 0.005782242 0.01963765 3398.567
## Max.cor.Y.TmSrs.poly.glm 0.004415477 0.02575099 3255.592
## Interact.High.cor.Y.glm 0.004259063 0.02653463 2857.963
## Low.cor.X.glm 0.005126348 0.01436941 2101.413
## All.X.glm 0.009143116 0.03090329 2161.079
## All.X.no.rnorm.rpart 0.008744104 0.05072074 NA
## Rank9.Bayes.glm.glm 0.005080323 0.01379869 2129.829
## All.X.2.glm 0.007840729 0.02319650 2243.694rm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 3 fit.models_1_rpart 3 0 822.337 1292.722 470.385
## 4 fit.models_1_end 4 0 1292.722 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 622.162 1292.73 670.569
## 12 fit.models 7 2 1292.731 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_fitobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_OOBobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
# tmp_models_df <- orderBy(~model_id, glb_models_df)
# rownames(tmp_models_df) <- seq(1, nrow(tmp_models_df))
# all.equal(subset(tmp_models_df[, names(stats_df)], model_id != "Random.myrandom_classfr"),
# subset(stats_df, model_id != "Random.myrandom_classfr"))
# print(subset(tmp_models_df[, names(stats_df)], model_id != "Random.myrandom_classfr")[, c("model_id", "max.Accuracy.fit")])
# print(subset(stats_df, model_id != "Random.myrandom_classfr")[, c("model_id", "max.Accuracy.fit")])
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id", grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df), grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id model_method
## MFO.myMFO_classfr MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.glm Max.cor.Y.glm glm
## Max.cor.Y.TmSrs.poly.glm Max.cor.Y.TmSrs.poly.glm glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm glm
## Low.cor.X.glm Low.cor.X.glm glm
## All.X.glm All.X.glm glm
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## Rank9.Bayes.glm.glm Rank9.Bayes.glm.glm glm
## All.X.2.glm All.X.2.glm glm
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart A.nuppr.log, WordCount.log
## Max.cor.Y.cv.0.cp.0.rpart A.nuppr.log, WordCount.log
## Max.cor.Y.rpart A.nuppr.log, WordCount.log
## Max.cor.Y.glm A.nuppr.log, WordCount.log
## Max.cor.Y.TmSrs.poly.glm A.nuppr.log, WordCount.log, PubDate.day.minutes.poly.1, PubDate.day.minutes.poly.2, PubDate.day.minutes.poly.3, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.5
## Interact.High.cor.Y.glm A.nuppr.log, WordCount.log, A.nuppr.log:A.ratio.sum.TfIdf.nwrds, A.nuppr.log:S.sum.TfIdf, A.nuppr.log:PubDate.day.minutes.poly.1, A.nuppr.log:A.ratio.nstopwrds.nwrds, A.nuppr.log:S.npnct19.log, A.nuppr.log:A.T.make, A.nuppr.log:A.npnct01.log, A.nuppr.log:H.npnct06.log, A.nuppr.log:A.T.can, A.nuppr.log:S.npnct21.log, A.nuppr.log:A.T.said, A.nuppr.log:A.npnct21.log, A.nuppr.log:A.T.one, A.nuppr.log:A.npnct07.log, A.nuppr.log:A.npnct03.log, A.nuppr.log:A.P.http, A.nuppr.log:A.npnct02.log, A.nuppr.log:A.T.appear, A.nuppr.log:A.npnct17.log, A.nuppr.log:A.npnct14.log, A.nuppr.log:A.P.year.colon, A.nuppr.log:A.T.obama, A.nuppr.log:A.npnct20.log, A.nuppr.log:A.P.first.draft, A.nuppr.log:A.T.take, A.nuppr.log:A.npnct06.log, A.nuppr.log:A.T.time, A.nuppr.log:A.T.new, A.nuppr.log:H.npnct04.log, A.nuppr.log:A.P.metropolitan.diary.colon, A.nuppr.log:H.P.today.in.politic, A.nuppr.log:H.P.what.we.are, A.nuppr.log:A.npnct12.log, A.nuppr.log:H.T.today, A.nuppr.log:S.T.year, A.nuppr.log:S.T.will, A.nuppr.log:S.T.senat, A.nuppr.log:A.T.show, A.nuppr.log:A.T.day, A.nuppr.log:H.T.first, A.nuppr.log:A.npnct28.log, A.nuppr.log:H.T.morn, A.nuppr.log:H.T.daili, A.nuppr.log:A.P.daily.clip.report, A.nuppr.log:S.T.first, A.nuppr.log:S.T.newyork, A.nuppr.log:S.T.report, A.nuppr.log:S.T.compani, A.nuppr.log:S.T.word, A.nuppr.log:S.T.newyorktim, A.nuppr.log:S.npnct13.log, A.nuppr.log:A.T.share, A.nuppr.log:S.T.articl, A.nuppr.log:H.npnct14.log, A.nuppr.log:H.T.X2015, A.nuppr.log:A.T.diari, A.nuppr.log:A.npnct04.log, A.nuppr.log:S.T.fashion, A.nuppr.log:A.T.tribun, A.nuppr.log:A.T.herald, A.nuppr.log:A.npnct15.log, A.nuppr.log:S.T.photo, A.nuppr.log:S.T.intern, A.nuppr.log:A.T.archiv, A.nuppr.log:A.P.fashion.week, A.nuppr.log:H.P.year.colon, A.nuppr.log:H.T.fashion, A.nuppr.log:S.T.week, A.nuppr.log:A.npnct11.log, A.nuppr.log:A.nstopwrds.log, A.nuppr.log:A.ndgts.log, A.nuppr.log:H.nwrds.unq.log, A.nuppr.log:H.nchrs.log, A.nuppr.log:A.nwrds.log, A.nuppr.log:S.nchrs.log, A.nuppr.log:S.nwrds.unq.log, A.nuppr.log:A.nuppr.log
## Low.cor.X.glm WordCount.log, A.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, H.npnct19.log, A.ratio.nstopwrds.nwrds, PubDate.wkend, H.P.recap.colon, H.P.quandary, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, H.P.no.comment.colon, S.npnct19.log, H.P.facts.figures, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.P.readers.respond, A.T.make, H.ratio.nstopwrds.nwrds, H.T.get, H.npnct06.log, A.npnct01.log, A.T.can, PubDate.day.minutes.poly.3, H.T.ebola, H.npnct01.log, A.T.said, H.T.make, H.npnct11.log, A.T.one, H.P.s.notebook, H.T.take, A.npnct16.log, S.npnct16.log, A.T.presid, S.T.presid, S.npnct08.log, A.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, H.P.friday.night.music, H.T.say, H.T.obama, H.T.bank, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, A.npnct07.log, H.npnct12.log, A.npnct03.log, H.T.big, A.P.year.colon, A.T.obama, A.npnct20.log, H.npnct02.log, H.T.test, S.npnct14.log, A.P.first.draft, H.P.on.this.day, A.T.take, A.npnct06.log, A.npnct14.log, A.T.time, H.T.newyorktim, H.npnct13.log, H.T.deal, A.T.new, A.P.metropolitan.diary.colon, H.P.verbatim.colon, H.T.china, H.T.art, PubDate.minute.fctr, S.T.year, A.npnct12.log, H.P.what.we.are, S.T.will, A.T.appear, PubDate.wkday.fctr, H.T.pictur, H.T.new, S.T.senat, A.T.show, H.P.today.in.smallbusiness, A.T.day, H.T.news, S.T.first, H.T.first, H.T.X2014, S.T.newyork, S.T.report, S.T.compani, S.T.word, H.T.morn, H.T.busi, S.T.newyorktim, A.T.share, H.npnct04.log, S.npnct13.log, S.T.articl, H.T.newyork, PubDate.day.minutes.poly.5, H.T.today, H.T.day, A.npnct04.log, H.T.daili, H.T.week, S.T.photo, A.npnct15.log, H.P.year.colon, H.T.fashion, H.npnct15.log, S.T.fashion, S.T.week, H.nstopwrds.log, H.npnct28.log, A.npnct11.log, H.ndgts.log, A.ndgts.log, H.nwrds.unq.log, A.nuppr.log, myCategory.fctr:.clusterid.fctr
## All.X.glm WordCount.log, A.ratio.sum.TfIdf.nwrds, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, H.P.recap.colon, H.P.quandary, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, H.P.no.comment.colon, S.npnct19.log, H.P.facts.figures, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.P.readers.respond, A.T.make, S.T.make, H.ratio.nstopwrds.nwrds, H.T.get, H.npnct06.log, S.npnct01.log, A.T.can, H.npnct16.log, S.T.can, PubDate.day.minutes.poly.3, H.T.ebola, H.npnct01.log, A.T.said, S.T.said, H.T.make, H.npnct11.log, A.T.one, S.T.one, H.P.s.notebook, H.T.take, S.npnct16.log, A.T.presid, S.T.presid, S.npnct08.log, PubDate.last100.log, .rnorm, H.npnct05.log, H.P.friday.night.music, H.T.say, H.T.obama, H.T.bank, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, H.T.word, H.T.big, A.npnct02.log, A.npnct17.log, S.P.year.colon, S.T.obama, A.T.obama, S.npnct20.log, H.npnct02.log, H.T.test, H.P.on.this.day, S.P.first.draft, S.T.take, A.T.take, S.npnct06.log, A.npnct14.log, S.T.time, A.T.time, H.T.newyorktim, H.npnct13.log, H.T.deal, S.T.new, A.T.new, H.T.billion, S.P.metropolitan.diary.colon, H.T.polit, H.P.verbatim.colon, H.T.china, H.T.art, PubDate.minute.fctr, H.T.read, S.npnct12.log, H.P.today.in.politic, A.T.year, S.T.year, H.P.what.we.are, A.T.will, S.T.will, A.T.appear, S.T.appear, PubDate.wkday.fctr, H.T.pictur, H.T.new, A.T.senat, S.T.senat, S.T.show, A.T.show, H.P.today.in.smallbusiness, S.T.day, A.T.day, S.npnct28.log, A.npnct28.log, H.P.daily.clip.report, H.T.clip, A.T.first, H.T.news, S.T.first, H.T.first, H.T.X2014, A.T.newyork, S.T.newyork, A.T.report, A.T.compani, S.T.report, S.T.compani, A.T.word, S.T.word, H.T.morn, H.T.busi, A.T.newyorktim, S.T.newyorktim, A.npnct13.log, A.T.share, S.T.share, H.npnct04.log, S.npnct13.log, A.T.articl, S.T.articl, H.T.newyork, PubDate.day.minutes.poly.5, H.T.today, H.T.springsumm, H.T.day, H.npnct14.log, A.T.diari, S.T.diari, H.T.report, S.npnct04.log, H.T.daili, H.T.X2015, A.T.herald, S.T.herald, S.npnct15.log, H.T.week, A.T.photo, S.T.photo, A.T.intern, S.T.intern, A.T.tribun, S.T.tribun, S.P.fashion.week, A.T.archiv, S.T.archiv, H.P.fashion.week, H.P.year.colon, H.T.fashion, H.npnct15.log, A.T.fashion, S.T.fashion, A.T.week, S.T.week, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr
## All.X.no.rnorm.rpart WordCount.log, A.ratio.sum.TfIdf.nwrds, S.ratio.sum.TfIdf.nwrds, H.ratio.sum.TfIdf.nwrds, myCategory.fctr, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr, H.npnct19.log, A.ratio.nstopwrds.nwrds, S.ratio.nstopwrds.nwrds, PubDate.wkend, H.P.recap.colon, H.P.quandary, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, H.P.no.comment.colon, S.npnct19.log, H.P.facts.figures, H.npnct08.log, PubDate.last10.log, PubDate.last1.log, H.P.readers.respond, A.T.make, S.T.make, H.ratio.nstopwrds.nwrds, H.T.get, H.npnct06.log, S.npnct01.log, A.T.can, H.npnct16.log, S.T.can, PubDate.day.minutes.poly.3, H.T.ebola, H.npnct01.log, A.T.said, S.T.said, H.T.make, H.npnct11.log, A.T.one, S.T.one, H.P.s.notebook, H.T.take, S.npnct16.log, A.T.presid, S.T.presid, S.npnct08.log, PubDate.last100.log, H.npnct05.log, H.P.friday.night.music, H.T.say, H.T.obama, H.T.bank, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, H.T.word, H.T.big, A.npnct02.log, A.npnct17.log, S.P.year.colon, S.T.obama, A.T.obama, S.npnct20.log, H.npnct02.log, H.T.test, H.P.on.this.day, S.P.first.draft, S.T.take, A.T.take, S.npnct06.log, A.npnct14.log, S.T.time, A.T.time, H.T.newyorktim, H.npnct13.log, H.T.deal, S.T.new, A.T.new, H.T.billion, S.P.metropolitan.diary.colon, H.T.polit, H.P.verbatim.colon, H.T.china, H.T.art, PubDate.minute.fctr, H.T.read, S.npnct12.log, H.P.today.in.politic, A.T.year, S.T.year, H.P.what.we.are, A.T.will, S.T.will, A.T.appear, S.T.appear, PubDate.wkday.fctr, H.T.pictur, H.T.new, A.T.senat, S.T.senat, S.T.show, A.T.show, H.P.today.in.smallbusiness, S.T.day, A.T.day, S.npnct28.log, A.npnct28.log, H.P.daily.clip.report, H.T.clip, A.T.first, H.T.news, S.T.first, H.T.first, H.T.X2014, A.T.newyork, S.T.newyork, A.T.report, A.T.compani, S.T.report, S.T.compani, A.T.word, S.T.word, H.T.morn, H.T.busi, A.T.newyorktim, S.T.newyorktim, A.npnct13.log, A.T.share, S.T.share, H.npnct04.log, S.npnct13.log, A.T.articl, S.T.articl, H.T.newyork, PubDate.day.minutes.poly.5, H.T.today, H.T.springsumm, H.T.day, H.npnct14.log, A.T.diari, S.T.diari, H.T.report, S.npnct04.log, H.T.daili, H.T.X2015, A.T.herald, S.T.herald, S.npnct15.log, H.T.week, A.T.photo, S.T.photo, A.T.intern, S.T.intern, A.T.tribun, S.T.tribun, S.P.fashion.week, A.T.archiv, S.T.archiv, H.P.fashion.week, H.P.year.colon, H.T.fashion, H.npnct15.log, A.T.fashion, S.T.fashion, A.T.week, S.T.week, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nuppr.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, H.nwrds.unq.log, S.nchrs.log, A.nwrds.unq.log, S.nwrds.unq.log, S.nuppr.log, myCategory.fctr:.clusterid.fctr
## Rank9.Bayes.glm.glm myCategory.fctr*WordCount.log, myCategory.fctr:.clusterid.fctr, myCategory.fctr*H.npnct19.log, myCategory.fctr:PubDate.day.minutes.poly.1, myCategory.fctr:PubDate.day.minutes.poly.2, myCategory.fctr:PubDate.day.minutes.poly.3, myCategory.fctr:PubDate.day.minutes.poly.4, myCategory.fctr:PubDate.day.minutes.poly.5
## All.X.2.glm WordCount.log, myCategory.fctr, PubDate.day.minutes.poly.1, H.sum.TfIdf, S.sum.TfIdf, A.sum.TfIdf, PubDate.hour.fctr, H.npnct19.log, PubDate.wkend, H.P.recap.colon, H.P.quandary, PubDate.day.minutes.poly.4, PubDate.day.minutes.poly.2, H.P.no.comment.colon, H.P.facts.figures, PubDate.last10.log, PubDate.last1.log, A.T.make, S.T.make, H.T.get, H.npnct06.log, S.npnct01.log, A.T.can, H.npnct16.log, S.T.can, PubDate.day.minutes.poly.3, H.T.ebola, H.npnct01.log, A.T.said, S.T.said, H.T.make, H.npnct11.log, A.T.one, S.T.one, H.P.s.notebook, H.T.take, S.npnct16.log, A.T.presid, S.T.presid, PubDate.last100.log, .rnorm, H.npnct05.log, H.P.friday.night.music, H.T.say, H.T.obama, H.T.bank, PubDate.date.fctr, PubDate.second.fctr, H.npnct07.log, S.npnct07.log, S.npnct03.log, A.npnct18.log, H.npnct12.log, H.T.word, H.T.big, A.npnct02.log, A.npnct17.log, S.P.year.colon, S.T.obama, A.T.obama, S.npnct20.log, H.npnct02.log, H.T.test, H.P.on.this.day, S.P.first.draft, S.T.take, A.T.take, S.npnct06.log, A.npnct14.log, S.T.time, A.T.time, H.T.newyorktim, H.T.deal, S.T.new, A.T.new, H.T.billion, S.P.metropolitan.diary.colon, H.T.polit, H.P.verbatim.colon, H.T.china, H.T.art, PubDate.minute.fctr, H.T.read, S.npnct12.log, H.P.today.in.politic, A.T.year, S.T.year, H.P.what.we.are, A.T.will, S.T.will, A.T.appear, S.T.appear, PubDate.wkday.fctr, H.T.pictur, H.T.new, A.T.senat, S.T.senat, S.T.show, A.T.show, H.P.today.in.smallbusiness, S.T.day, A.T.day, S.npnct28.log, A.npnct28.log, H.P.daily.clip.report, H.T.clip, A.T.first, H.T.news, S.T.first, H.T.first, H.T.X2014, A.T.newyork, S.T.newyork, A.T.report, A.T.compani, S.T.report, S.T.compani, A.T.word, S.T.word, H.T.morn, H.T.busi, A.T.newyorktim, S.T.newyorktim, A.T.share, S.T.share, H.npnct04.log, A.T.articl, S.T.articl, H.T.newyork, PubDate.day.minutes.poly.5, H.T.today, H.T.springsumm, H.T.day, H.npnct14.log, A.T.diari, S.T.diari, H.T.report, S.npnct04.log, H.T.daili, H.T.X2015, A.T.herald, S.T.herald, H.T.week, A.T.photo, S.T.photo, A.T.intern, S.T.intern, A.T.tribun, S.T.tribun, S.P.fashion.week, A.T.archiv, S.T.archiv, H.P.fashion.week, H.P.year.colon, H.T.fashion, A.T.fashion, S.T.fashion, A.T.week, S.T.week, H.nstopwrds.log, H.npnct28.log, S.npnct11.log, S.nstopwrds.log, A.nstopwrds.log, H.ndgts.log, S.ndgts.log, H.nwrds.log, H.nchrs.log, S.nwrds.log, A.nwrds.log, S.nchrs.log, myCategory.fctr:.clusterid.fctr
## max.nTuningRuns max.auc.fit
## MFO.myMFO_classfr 0 0.5000000
## Random.myrandom_classfr 0 0.5072166
## Max.cor.Y.cv.0.rpart 0 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0 0.8239196
## Max.cor.Y.rpart 3 0.5000000
## Max.cor.Y.glm 1 0.7879886
## Max.cor.Y.TmSrs.poly.glm 1 0.8146498
## Interact.High.cor.Y.glm 1 0.8805672
## Low.cor.X.glm 1 0.9609008
## All.X.glm 1 0.9637398
## All.X.no.rnorm.rpart 3 0.7277461
## Rank9.Bayes.glm.glm 1 0.9592523
## All.X.2.glm 1 0.9573387
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.1 0.2867534
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.3 0.5137615
## Max.cor.Y.rpart 0.5 0.0000000
## Max.cor.Y.glm 0.2 0.4581986
## Max.cor.Y.TmSrs.poly.glm 0.3 0.5006289
## Interact.High.cor.Y.glm 0.3 0.5912409
## Low.cor.X.glm 0.3 0.7662740
## All.X.glm 0.3 0.7797030
## All.X.no.rnorm.rpart 0.7 0.5978351
## Rank9.Bayes.glm.glm 0.3 0.7565589
## All.X.2.glm 0.3 0.7640867
## max.Accuracy.fit max.Kappa.fit max.auc.OOB
## MFO.myMFO_classfr 0.8326257 0.0000000 0.5000000
## Random.myrandom_classfr 0.1673743 0.0000000 0.4877001
## Max.cor.Y.cv.0.rpart 0.8326257 0.0000000 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8341899 0.4138559 0.7482444
## Max.cor.Y.rpart 0.8232385 0.1140417 0.5000000
## Max.cor.Y.glm 0.8283787 0.1138834 0.7836398
## Max.cor.Y.TmSrs.poly.glm 0.8373175 0.2130159 0.8083559
## Interact.High.cor.Y.glm 0.8643586 0.4117899 0.8577584
## Low.cor.X.glm 0.9032400 0.6385029 0.9169373
## All.X.glm 0.8931847 0.6089725 0.9084175
## All.X.no.rnorm.rpart 0.8992181 0.5798643 0.7084504
## Rank9.Bayes.glm.glm 0.8884929 0.6023604 0.9326203
## All.X.2.glm 0.8929616 0.6054340 0.9104268
## opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.1 0.2865473
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.2 0.4240506
## Max.cor.Y.rpart 0.5 0.0000000
## Max.cor.Y.glm 0.2 0.4413240
## Max.cor.Y.TmSrs.poly.glm 0.2 0.5005192
## Interact.High.cor.Y.glm 0.3 0.5565670
## Low.cor.X.glm 0.4 0.7230321
## All.X.glm 0.4 0.7124464
## All.X.no.rnorm.rpart 0.7 0.5650558
## Rank9.Bayes.glm.glm 0.5 0.7445255
## All.X.2.glm 0.4 0.7225434
## max.Accuracy.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.8327662 0.0000000
## Random.myrandom_classfr 0.1672338 0.0000000
## Max.cor.Y.cv.0.rpart 0.8327662 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.7345649 0.2680784
## Max.cor.Y.rpart 0.8327662 0.0000000
## Max.cor.Y.glm 0.7292173 0.2846066
## Max.cor.Y.TmSrs.poly.glm 0.7661643 0.3637267
## Interact.High.cor.Y.glm 0.8342246 0.4560113
## Low.cor.X.glm 0.9076325 0.6676067
## All.X.glm 0.9022849 0.6536059
## All.X.no.rnorm.rpart 0.8862421 0.5054039
## Rank9.Bayes.glm.glm 0.9149246 0.6934914
## All.X.2.glm 0.9066602 0.6664381
## inv.elapsedtime.everything inv.elapsedtime.final
## MFO.myMFO_classfr 1.063829787 3.333333e+02
## Random.myrandom_classfr 2.762430939 1.000000e+03
## Max.cor.Y.cv.0.rpart 1.133786848 1.176471e+01
## Max.cor.Y.cv.0.cp.0.rpart 1.494768311 1.265823e+01
## Max.cor.Y.rpart 0.684462697 1.149425e+01
## Max.cor.Y.glm 0.746825990 1.063830e+01
## Max.cor.Y.TmSrs.poly.glm 0.643500644 5.319149e+00
## Interact.High.cor.Y.glm 0.219442616 5.370569e-01
## Low.cor.X.glm 0.006757716 1.469054e-02
## All.X.glm 0.005470998 1.161508e-02
## All.X.no.rnorm.rpart 0.035356928 1.557390e-01
## Rank9.Bayes.glm.glm 0.004048780 9.379367e-03
## All.X.2.glm 0.005839382 1.292474e-02
## inv.aic.fit
## MFO.myMFO_classfr NA
## Random.myrandom_classfr NA
## Max.cor.Y.cv.0.rpart NA
## Max.cor.Y.cv.0.cp.0.rpart NA
## Max.cor.Y.rpart NA
## Max.cor.Y.glm 0.0002942416
## Max.cor.Y.TmSrs.poly.glm 0.0003071638
## Interact.High.cor.Y.glm 0.0003498996
## Low.cor.X.glm 0.0004758702
## All.X.glm 0.0004627318
## All.X.no.rnorm.rpart NA
## Rank9.Bayes.glm.glm 0.0004695213
## All.X.2.glm 0.0004456937print(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.## Warning in loop_apply(n, do.ply): Removed 4 rows containing missing values
## (geom_path).## Warning in loop_apply(n, do.ply): Removed 103 rows containing missing
## values (geom_point).## Warning in loop_apply(n, do.ply): Removed 6 rows containing missing values
## (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
#print(mltdCI_models_df)
# castCI_models_df <- dcast(mltdCI_models_df, value ~ type, fun.aggregate=sum)
# print(castCI_models_df)
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")],
all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
dev.off()## quartz_off_screen
## 2print(gp)
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)
[, c("model_id", glb_model_evl_criteria,
ifelse(glb_is_classification && glb_is_binomial,
"opt.prob.threshold.OOB", NULL))])## model_id max.Accuracy.OOB
## Rank9.Bayes.glm.glm Rank9.Bayes.glm.glm 0.9149246
## Low.cor.X.glm Low.cor.X.glm 0.9076325
## All.X.2.glm All.X.2.glm 0.9066602
## All.X.glm All.X.glm 0.9022849
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart 0.8862421
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm 0.8342246
## MFO.myMFO_classfr MFO.myMFO_classfr 0.8327662
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart 0.8327662
## Max.cor.Y.rpart Max.cor.Y.rpart 0.8327662
## Max.cor.Y.TmSrs.poly.glm Max.cor.Y.TmSrs.poly.glm 0.7661643
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart 0.7345649
## Max.cor.Y.glm Max.cor.Y.glm 0.7292173
## Random.myrandom_classfr Random.myrandom_classfr 0.1672338
## max.auc.OOB max.Kappa.OOB min.aic.fit
## Rank9.Bayes.glm.glm 0.9326203 0.6934914 2129.829
## Low.cor.X.glm 0.9169373 0.6676067 2101.413
## All.X.2.glm 0.9104268 0.6664381 2243.694
## All.X.glm 0.9084175 0.6536059 2161.079
## All.X.no.rnorm.rpart 0.7084504 0.5054039 NA
## Interact.High.cor.Y.glm 0.8577584 0.4560113 2857.963
## MFO.myMFO_classfr 0.5000000 0.0000000 NA
## Max.cor.Y.cv.0.rpart 0.5000000 0.0000000 NA
## Max.cor.Y.rpart 0.5000000 0.0000000 NA
## Max.cor.Y.TmSrs.poly.glm 0.8083559 0.3637267 3255.592
## Max.cor.Y.cv.0.cp.0.rpart 0.7482444 0.2680784 NA
## Max.cor.Y.glm 0.7836398 0.2846066 3398.567
## Random.myrandom_classfr 0.4877001 0.0000000 NA
## opt.prob.threshold.OOB
## Rank9.Bayes.glm.glm 0.5
## Low.cor.X.glm 0.4
## All.X.2.glm 0.4
## All.X.glm 0.4
## All.X.no.rnorm.rpart 0.7
## Interact.High.cor.Y.glm 0.3
## MFO.myMFO_classfr 0.5
## Max.cor.Y.cv.0.rpart 0.5
## Max.cor.Y.rpart 0.5
## Max.cor.Y.TmSrs.poly.glm 0.2
## Max.cor.Y.cv.0.cp.0.rpart 0.2
## Max.cor.Y.glm 0.2
## Random.myrandom_classfr 0.1print(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.## Warning in loop_apply(n, do.ply): Removed 44 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 6 rows containing missing values
## (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.auc.OOB - max.Kappa.OOB + min.aic.fit -
## opt.prob.threshold.OOBprint(sprintf("Best model id: %s", dsp_models_df[1, "model_id"]))## [1] "Best model id: Rank9.Bayes.glm.glm"if (is.null(glb_sel_mdl_id)) {
glb_sel_mdl_id <- dsp_models_df[1, "model_id"]
if (glb_sel_mdl_id == "Interact.High.cor.Y.glm") {
warning("glb_sel_mdl_id: Interact.High.cor.Y.glm; myextract_mdl_feats does not currently support interaction terms")
glb_sel_mdl_id <- dsp_models_df[2, "model_id"]
}
} else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])## Warning: not plotting observations with leverage one:
## 121, 2403
## Warning: not plotting observations with leverage one:
## 121, 2403
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2796 -0.2588 -0.0457 0.0000 3.8405
##
## Coefficients: (315 not defined because of singularities)
## Estimate
## (Intercept) -1.341e+01
## `myCategory.fctr#Multimedia#` -2.035e+01
## `myCategory.fctr#Opinion#Room For Debate` -2.502e+02
## `myCategory.fctr#Opinion#The Public Editor` 3.482e+01
## `myCategory.fctr#U.S.#Education` -1.316e+01
## `myCategory.fctrBusiness#Business Day#Dealbook` -9.530e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 1.498e+05
## `myCategory.fctrBusiness#Crosswords/Games#` 2.203e+03
## `myCategory.fctrBusiness#Technology#` -2.256e+00
## `myCategory.fctrCulture#Arts#` 5.106e+00
## `myCategory.fctrForeign#World#` -1.316e+01
## `myCategory.fctrForeign#World#Asia Pacific` -2.748e+01
## `myCategory.fctrMetro#N.Y. / Region#` 8.850e+00
## myCategory.fctrmyOther -1.316e+01
## `myCategory.fctrOpEd#Opinion#` 1.382e+01
## `myCategory.fctrScience#Health#` 1.559e+00
## `myCategory.fctrStyles##Fashion` -1.316e+01
## `myCategory.fctrStyles#U.S.#` 8.187e+00
## `myCategory.fctrTravel#Travel#` -1.806e+02
## `myCategory.fctrTStyle##` -4.818e+01
## WordCount.log 1.788e+00
## H.npnct19.log 2.025e+00
## `myCategory.fctr#Multimedia#:WordCount.log` -6.456e-01
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` -2.778e+00
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` -2.613e+00
## `myCategory.fctr#U.S.#Education:WordCount.log` -1.788e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 1.216e+00
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` -3.663e+00
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 8.665e-01
## `myCategory.fctrBusiness#Technology#:WordCount.log` 3.151e-01
## `myCategory.fctrCulture#Arts#:WordCount.log` -9.169e-01
## `myCategory.fctrForeign#World#:WordCount.log` -1.788e+00
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` -1.658e+00
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` -1.344e+00
## `myCategory.fctrmyOther:WordCount.log` -1.788e+00
## `myCategory.fctrOpEd#Opinion#:WordCount.log` -1.604e+00
## `myCategory.fctrScience#Health#:WordCount.log` 3.320e-01
## `myCategory.fctrStyles##Fashion:WordCount.log` -1.788e+00
## `myCategory.fctrStyles#U.S.#:WordCount.log` -9.319e-01
## `myCategory.fctrTravel#Travel#:WordCount.log` 1.232e+02
## `myCategory.fctrTStyle##:WordCount.log` -3.888e-02
## `myCategory.fctr##:.clusterid.fctr2` -3.483e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 2.333e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 7.535e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` -3.944e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` -9.485e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` -3.271e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` -5.136e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` -7.266e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` -1.302e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 6.222e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` -2.323e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` -1.115e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr3` -2.837e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` -2.802e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` -2.301e+01
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 1.578e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 1.013e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 9.297e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` -2.272e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` -6.342e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 3.587e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` -1.389e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 9.688e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 1.889e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 3.296e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` -1.219e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` -2.406e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` -9.778e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` -2.310e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` -2.297e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` -5.804e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` -1.621e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 6.565e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 6.370e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 2.639e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.611e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` -1.269e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 2.175e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` -4.243e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` -2.374e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` -1.326e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` -7.132e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 2.021e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` -2.300e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` -2.410e+01
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 8.277e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` -2.307e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` -3.259e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` -2.366e+01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` -2.291e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` -8.151e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` -1.187e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 1.395e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` -1.381e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` -4.497e-02
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 1.740e+00
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` -4.612e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` -2.487e+01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` -2.324e+01
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 1.626e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 1.406e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` -2.094e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` -3.573e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` 9.462e-02
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr##:.clusterid.fctr18` -1.197e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr18` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr18` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18` NA
## `myCategory.fctrmyOther:.clusterid.fctr18` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr18` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18` NA
## `myCategory.fctrTStyle##:.clusterid.fctr18` NA
## `myCategory.fctr##:.clusterid.fctr19` -2.968e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19` NA
## `myCategory.fctrmyOther:.clusterid.fctr19` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr19` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
## `myCategory.fctrTStyle##:.clusterid.fctr19` NA
## `myCategory.fctr##:.clusterid.fctr20` -2.556e+01
## `myCategory.fctr#Multimedia#:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20` NA
## `myCategory.fctrmyOther:.clusterid.fctr20` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr20` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
## `myCategory.fctr#Multimedia#:H.npnct19.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` -3.376e+01
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 7.941e+01
## `myCategory.fctr#U.S.#Education:H.npnct19.log` -2.025e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` -2.080e+00
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` -1.497e+01
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 1.134e+00
## `myCategory.fctrCulture#Arts#:H.npnct19.log` -4.771e-01
## `myCategory.fctrForeign#World#:H.npnct19.log` -2.025e+00
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` -1.553e+01
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 5.887e-01
## `myCategory.fctrmyOther:H.npnct19.log` -2.026e+00
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` -1.458e+00
## `myCategory.fctrScience#Health#:H.npnct19.log` -1.435e+00
## `myCategory.fctrStyles##Fashion:H.npnct19.log` -2.025e+00
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` -5.056e-01
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 1.159e+01
## `myCategory.fctrTStyle##:H.npnct19.log` -3.472e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.1` -5.120e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 1.048e+03
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 4.972e+04
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` -4.686e+04
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 4.746e-04
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 4.151e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 4.339e+08
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` -4.225e+05
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 6.954e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` -2.141e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 1.180e-05
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` -8.062e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 2.941e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` -2.226e-02
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 5.842e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` -8.100e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` -1.157e-06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 1.826e+00
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` -2.015e+05
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 1.187e+03
## `myCategory.fctr##:PubDate.day.minutes.poly.2` -5.758e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` -8.078e+02
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` -5.913e+04
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 8.710e+03
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 4.986e-04
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 2.694e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` -5.643e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 5.332e+05
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 1.647e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 3.335e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` -1.804e-05
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 4.750e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` -6.060e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 2.480e-02
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` -1.131e+02
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 5.994e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` -3.818e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` -9.089e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 1.887e+05
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` -1.005e+04
## `myCategory.fctr##:PubDate.day.minutes.poly.3` -6.653e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 2.066e+03
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 4.459e+04
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` -5.213e+04
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 4.025e-04
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` -1.892e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 5.273e+08
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` -4.345e+05
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` -1.339e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 2.025e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 1.978e-05
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` -1.147e+03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` -2.013e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` -2.884e-02
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 1.517e+02
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 3.660e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 1.873e-06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` -2.146e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` -2.546e+05
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 2.186e+03
## `myCategory.fctr##:PubDate.day.minutes.poly.4` -5.013e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` -1.122e+03
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` -1.921e+04
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 2.843e+03
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 2.610e-04
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 2.000e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` -2.325e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 2.020e+05
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` -2.335e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 1.702e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` -9.862e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` -2.360e+03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 3.355e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 1.218e-02
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` -1.013e+02
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` -7.091e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 3.147e-06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` -1.009e+02
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 8.849e+04
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` -4.228e+03
## `myCategory.fctr##:PubDate.day.minutes.poly.5` -3.890e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 1.007e+03
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 4.916e+03
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` -1.312e+04
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 8.998e-05
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` -1.659e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 1.669e+08
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` -5.268e+04
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 9.870e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 1.945e+00
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 6.945e-06
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` -1.039e+03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` -8.700e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` -7.869e-03
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 4.981e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 2.600e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 3.928e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` -2.077e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` -6.301e+04
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 3.340e+02
## Std. Error
## (Intercept) 1.583e+00
## `myCategory.fctr#Multimedia#` 2.042e+05
## `myCategory.fctr#Opinion#Room For Debate` 5.089e+02
## `myCategory.fctr#Opinion#The Public Editor` 3.611e+07
## `myCategory.fctr#U.S.#Education` 8.892e+05
## `myCategory.fctrBusiness#Business Day#Dealbook` 3.598e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 1.057e+06
## `myCategory.fctrBusiness#Crosswords/Games#` 5.804e+08
## `myCategory.fctrBusiness#Technology#` 4.167e+00
## `myCategory.fctrCulture#Arts#` 3.355e+00
## `myCategory.fctrForeign#World#` 7.373e+05
## `myCategory.fctrForeign#World#Asia Pacific` 2.593e+01
## `myCategory.fctrMetro#N.Y. / Region#` 2.431e+00
## myCategory.fctrmyOther 7.339e+05
## `myCategory.fctrOpEd#Opinion#` 1.763e+00
## `myCategory.fctrScience#Health#` 2.899e+00
## `myCategory.fctrStyles##Fashion` 5.887e+05
## `myCategory.fctrStyles#U.S.#` 2.612e+00
## `myCategory.fctrTravel#Travel#` 1.242e+07
## `myCategory.fctrTStyle##` 7.276e+01
## WordCount.log 2.309e-01
## H.npnct19.log 4.954e-01
## `myCategory.fctr#Multimedia#:WordCount.log` 2.491e+00
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` 8.305e-01
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` 8.463e+05
## `myCategory.fctr#U.S.#Education:WordCount.log` 3.414e+04
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 5.140e-01
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` 3.132e+00
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 1.115e+00
## `myCategory.fctrBusiness#Technology#:WordCount.log` 6.289e-01
## `myCategory.fctrCulture#Arts#:WordCount.log` 5.425e-01
## `myCategory.fctrForeign#World#:WordCount.log` 5.461e+04
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` 1.406e+00
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` 3.892e-01
## `myCategory.fctrmyOther:WordCount.log` 9.043e+04
## `myCategory.fctrOpEd#Opinion#:WordCount.log` 2.620e-01
## `myCategory.fctrScience#Health#:WordCount.log` 4.622e-01
## `myCategory.fctrStyles##Fashion:WordCount.log` 9.594e+04
## `myCategory.fctrStyles#U.S.#:WordCount.log` 3.811e-01
## `myCategory.fctrTravel#Travel#:WordCount.log` 2.233e+05
## `myCategory.fctrTStyle##:WordCount.log` 6.264e-01
## `myCategory.fctr##:.clusterid.fctr2` 1.083e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 5.595e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 7.372e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 9.176e-01
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
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## `myCategory.fctrScience#Health#:.clusterid.fctr2` 6.548e-01
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
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## `myCategory.fctr##:.clusterid.fctr3` 6.018e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 6.588e-01
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## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 7.445e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 5.739e+04
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 8.909e-01
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## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
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## `myCategory.fctr##:.clusterid.fctr5` 8.217e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 6.425e-01
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 7.002e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 8.002e-01
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## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
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## `myCategory.fctr##:.clusterid.fctr8` 5.693e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 7.523e-01
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## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
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## `myCategory.fctr##:.clusterid.fctr9` 6.927e-01
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## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 1.104e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 1.224e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` 7.607e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr##:.clusterid.fctr18` 1.162e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr18` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr18` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18` NA
## `myCategory.fctrmyOther:.clusterid.fctr18` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr18` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18` NA
## `myCategory.fctrTStyle##:.clusterid.fctr18` NA
## `myCategory.fctr##:.clusterid.fctr19` 1.517e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19` NA
## `myCategory.fctrmyOther:.clusterid.fctr19` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr19` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
## `myCategory.fctrTStyle##:.clusterid.fctr19` NA
## `myCategory.fctr##:.clusterid.fctr20` 1.037e+05
## `myCategory.fctr#Multimedia#:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20` NA
## `myCategory.fctrmyOther:.clusterid.fctr20` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr20` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
## `myCategory.fctr#Multimedia#:H.npnct19.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 1.282e+05
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 7.863e+05
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 1.034e+05
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 3.035e+00
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 1.455e+02
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 1.816e+00
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 1.231e+00
## `myCategory.fctrForeign#World#:H.npnct19.log` 3.015e+05
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 3.242e+05
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 1.547e+00
## `myCategory.fctrmyOther:H.npnct19.log` 3.496e+05
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 9.077e-01
## `myCategory.fctrScience#Health#:H.npnct19.log` 1.229e+00
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 2.212e+05
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 1.061e+00
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 3.314e+05
## `myCategory.fctrTStyle##:H.npnct19.log` 5.138e+05
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 4.971e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 4.913e+07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 9.919e+04
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 1.135e+10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 1.366e+08
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 1.870e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 2.722e+09
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 1.117e+11
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 1.831e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 3.162e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 2.060e+08
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 6.693e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 4.736e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 1.623e+08
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 4.003e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 5.031e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 4.150e+06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 7.196e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 3.822e+09
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 6.136e+04
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 6.523e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 3.220e+07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 1.140e+05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 1.046e+10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 1.529e+08
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 1.704e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 3.890e+08
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 1.402e+11
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 1.767e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 2.910e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 1.216e+08
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 4.766e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 5.972e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 8.265e+07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 6.365e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 3.000e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 4.506e+06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 8.826e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 3.610e+09
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 2.436e+04
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 6.687e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 5.331e+07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 9.227e+04
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 1.266e+10
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 1.222e+08
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 1.654e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 3.308e+09
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 1.140e+11
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 1.621e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 2.773e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 2.386e+08
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 8.913e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 5.615e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 2.149e+08
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 8.191e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 2.840e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 5.642e+06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 9.538e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 4.779e+09
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 6.876e+04
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 4.388e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 2.329e+07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 3.797e+04
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 4.052e+09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 7.049e+07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 1.952e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 1.625e+08
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 5.304e+10
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 1.786e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 3.055e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 6.305e+07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 1.541e+03
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 4.057e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 5.298e+07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 5.724e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 4.226e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 6.533e+06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 5.548e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 1.620e+09
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 9.340e+03
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 4.183e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 3.025e+07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 1.217e+04
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 2.748e+09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 2.246e+07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 1.792e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 1.046e+09
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 1.386e+10
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 1.728e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 2.543e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 7.355e+07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 7.177e+02
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.547e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 7.884e+07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 3.423e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 3.363e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 4.739e+06
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 6.776e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 1.169e+09
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 1.702e+04
## z value
## (Intercept) -8.466
## `myCategory.fctr#Multimedia#` 0.000
## `myCategory.fctr#Opinion#Room For Debate` -0.492
## `myCategory.fctr#Opinion#The Public Editor` 0.000
## `myCategory.fctr#U.S.#Education` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook` -2.649
## `myCategory.fctrBusiness#Business Day#Small Business` 0.142
## `myCategory.fctrBusiness#Crosswords/Games#` 0.000
## `myCategory.fctrBusiness#Technology#` -0.541
## `myCategory.fctrCulture#Arts#` 1.522
## `myCategory.fctrForeign#World#` 0.000
## `myCategory.fctrForeign#World#Asia Pacific` -1.060
## `myCategory.fctrMetro#N.Y. / Region#` 3.640
## myCategory.fctrmyOther 0.000
## `myCategory.fctrOpEd#Opinion#` 7.835
## `myCategory.fctrScience#Health#` 0.538
## `myCategory.fctrStyles##Fashion` 0.000
## `myCategory.fctrStyles#U.S.#` 3.134
## `myCategory.fctrTravel#Travel#` 0.000
## `myCategory.fctrTStyle##` -0.662
## WordCount.log 7.744
## H.npnct19.log 4.089
## `myCategory.fctr#Multimedia#:WordCount.log` -0.259
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` -3.344
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` 0.000
## `myCategory.fctr#U.S.#Education:WordCount.log` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 2.365
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` -1.170
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 0.777
## `myCategory.fctrBusiness#Technology#:WordCount.log` 0.501
## `myCategory.fctrCulture#Arts#:WordCount.log` -1.690
## `myCategory.fctrForeign#World#:WordCount.log` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` -1.179
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` -3.454
## `myCategory.fctrmyOther:WordCount.log` 0.000
## `myCategory.fctrOpEd#Opinion#:WordCount.log` -6.124
## `myCategory.fctrScience#Health#:WordCount.log` 0.718
## `myCategory.fctrStyles##Fashion:WordCount.log` 0.000
## `myCategory.fctrStyles#U.S.#:WordCount.log` -2.445
## `myCategory.fctrTravel#Travel#:WordCount.log` 0.001
## `myCategory.fctrTStyle##:WordCount.log` -0.062
## `myCategory.fctr##:.clusterid.fctr2` -3.215
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.417
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 1.022
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` -0.430
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` -2.288
## `myCategory.fctrScience#Health#:.clusterid.fctr2` -0.499
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` -0.992
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` -1.207
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## `myCategory.fctrmyOther:.clusterid.fctr18` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr18` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18` NA
## `myCategory.fctrTStyle##:.clusterid.fctr18` NA
## `myCategory.fctr##:.clusterid.fctr19` -0.196
## `myCategory.fctr#Multimedia#:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19` NA
## `myCategory.fctrmyOther:.clusterid.fctr19` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr19` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
## `myCategory.fctrTStyle##:.clusterid.fctr19` NA
## `myCategory.fctr##:.clusterid.fctr20` 0.000
## `myCategory.fctr#Multimedia#:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20` NA
## `myCategory.fctrmyOther:.clusterid.fctr20` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr20` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
## `myCategory.fctr#Multimedia#:H.npnct19.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 0.000
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 0.000
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` -0.685
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` -0.103
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` NA
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 0.624
## `myCategory.fctrCulture#Arts#:H.npnct19.log` -0.387
## `myCategory.fctrForeign#World#:H.npnct19.log` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 0.000
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 0.381
## `myCategory.fctrmyOther:H.npnct19.log` 0.000
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` -1.606
## `myCategory.fctrScience#Health#:H.npnct19.log` -1.168
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 0.000
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` -0.477
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 0.000
## `myCategory.fctrTStyle##:H.npnct19.log` 0.000
## `myCategory.fctr##:PubDate.day.minutes.poly.1` -1.030
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 0.000
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 0.501
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 0.000
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 2.220
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 0.159
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 0.000
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 0.380
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` -0.677
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` -1.204
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 0.621
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 0.000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 1.460
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` -1.610
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 0.000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 0.025
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 0.000
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 0.019
## `myCategory.fctr##:PubDate.day.minutes.poly.2` -0.883
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 0.000
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` -0.519
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 0.000
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 1.581
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` -0.145
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 0.000
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 0.932
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 1.146
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 0.997
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` -1.015
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 0.000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` -1.778
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 1.998
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 0.000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` -1.030
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 0.000
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` -0.413
## `myCategory.fctr##:PubDate.day.minutes.poly.3` -0.995
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 0.000
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 0.483
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 0.000
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` -1.144
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 0.159
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 0.000
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` -0.826
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 0.730
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` -1.286
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` -0.359
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 0.000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 1.852
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 1.289
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 0.000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` -0.225
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 0.000
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 0.032
## `myCategory.fctr##:PubDate.day.minutes.poly.4` -1.142
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 0.000
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` -0.506
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 0.000
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 1.025
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` -0.143
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 0.000
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` -1.307
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 0.557
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` -1.531
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 0.827
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 0.000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` -1.770
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` -1.678
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 0.000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` -1.819
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 0.000
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` -0.453
## `myCategory.fctr##:PubDate.day.minutes.poly.5` -0.930
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 0.000
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 0.404
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 0.000
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 0.000
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` -0.926
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 0.160
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 0.000
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 0.571
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 0.077
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 0.000
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` -1.448
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` -0.245
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 0.000
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 1.455
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 0.773
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 0.000
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` -0.307
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 0.000
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 0.020
## Pr(>|z|)
## (Intercept) < 2e-16
## `myCategory.fctr#Multimedia#` 0.999920
## `myCategory.fctr#Opinion#Room For Debate` 0.622920
## `myCategory.fctr#Opinion#The Public Editor` 0.999999
## `myCategory.fctr#U.S.#Education` 0.999988
## `myCategory.fctrBusiness#Business Day#Dealbook` 0.008079
## `myCategory.fctrBusiness#Business Day#Small Business` 0.887284
## `myCategory.fctrBusiness#Crosswords/Games#` 0.999997
## `myCategory.fctrBusiness#Technology#` 0.588243
## `myCategory.fctrCulture#Arts#` 0.128017
## `myCategory.fctrForeign#World#` 0.999986
## `myCategory.fctrForeign#World#Asia Pacific` 0.289254
## `myCategory.fctrMetro#N.Y. / Region#` 0.000273
## myCategory.fctrmyOther 0.999986
## `myCategory.fctrOpEd#Opinion#` 4.68e-15
## `myCategory.fctrScience#Health#` 0.590711
## `myCategory.fctrStyles##Fashion` 0.999982
## `myCategory.fctrStyles#U.S.#` 0.001723
## `myCategory.fctrTravel#Travel#` 0.999988
## `myCategory.fctrTStyle##` 0.507869
## WordCount.log 9.61e-15
## H.npnct19.log 4.34e-05
## `myCategory.fctr#Multimedia#:WordCount.log` 0.795509
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` 0.000824
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` 0.999998
## `myCategory.fctr#U.S.#Education:WordCount.log` 0.999958
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 0.018036
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` 0.242151
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 0.437140
## `myCategory.fctrBusiness#Technology#:WordCount.log` 0.616403
## `myCategory.fctrCulture#Arts#:WordCount.log` 0.091002
## `myCategory.fctrForeign#World#:WordCount.log` 0.999974
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` 0.238334
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` 0.000552
## `myCategory.fctrmyOther:WordCount.log` 0.999984
## `myCategory.fctrOpEd#Opinion#:WordCount.log` 9.14e-10
## `myCategory.fctrScience#Health#:WordCount.log` 0.472636
## `myCategory.fctrStyles##Fashion:WordCount.log` 0.999985
## `myCategory.fctrStyles#U.S.#:WordCount.log` 0.014483
## `myCategory.fctrTravel#Travel#:WordCount.log` 0.999560
## `myCategory.fctrTStyle##:WordCount.log` 0.950504
## `myCategory.fctr##:.clusterid.fctr2` 0.001305
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.676703
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 0.306751
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 0.667320
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 0.022141
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 0.617452
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 0.321000
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 0.227280
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 0.843366
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 0.403301
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 0.999677
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 0.005374
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 0.663807
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 0.607186
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 0.999646
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 0.859391
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 0.195202
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 0.409776
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 0.700964
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 0.293977
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 0.623820
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` 0.090918
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 0.131610
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 0.850063
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 0.779382
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 0.013922
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 0.731113
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 0.912850
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 0.999786
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##
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## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` *
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1`
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1`
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## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1`
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## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1`
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## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1`
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## `myCategory.fctr##:PubDate.day.minutes.poly.2`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2`
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2`
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2`
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2`
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` .
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` *
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2`
## `myCategory.fctr##:PubDate.day.minutes.poly.3`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3`
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3`
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3`
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3`
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` .
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3`
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3`
## `myCategory.fctr##:PubDate.day.minutes.poly.4`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4`
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4`
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4`
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4`
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` .
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` .
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` .
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4`
## `myCategory.fctr##:PubDate.day.minutes.poly.5`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5`
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5`
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5`
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5`
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5`
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5`
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5`
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5`
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5`
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5`
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5`
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4042.7 on 4474 degrees of freedom
## Residual deviance: 1679.8 on 4250 degrees of freedom
## AIC: 2129.8
##
## Number of Fisher Scoring iterations: 25## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
if (glb_is_regression) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, rsp_var_out, smooth=TRUE))
df[, paste0(rsp_var_out, ".err")] <-
abs(df[, rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(glb_rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$model_id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
}
return(df)
}
glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, glb_sel_mdl_id, glb_rsp_var_out)## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleadingpredct_accurate_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".accurate")
glb_OOBobs_df[, predct_accurate_var_name] <-
(glb_OOBobs_df[, glb_rsp_var] ==
glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)])
#stop(here"); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## importance
## `myCategory.fctrOpEd#Opinion#` 1.000000e+02
## WordCount.log 9.884070e+01
## `myCategory.fctrOpEd#Opinion#:WordCount.log` 7.815739e+01
## H.npnct19.log 5.218153e+01
## `myCategory.fctrMetro#N.Y. / Region#` 4.645295e+01
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` 4.408266e+01
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` 4.268518e+01
## `myCategory.fctr##:.clusterid.fctr2` 4.103112e+01
## `myCategory.fctrStyles#U.S.#` 4.000256e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 3.552842e+01
## `myCategory.fctrBusiness#Business Day#Dealbook` 3.380594e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 3.138751e+01
## `myCategory.fctrStyles#U.S.#:WordCount.log` 3.120595e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 3.018292e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 2.920092e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 2.832896e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 2.696428e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 2.549705e+01
## `myCategory.fctr##:.clusterid.fctr16` 2.419378e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 2.363321e+01
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 2.321685e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 2.268658e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 2.259505e+01
## `myCategory.fctr##:.clusterid.fctr5` 2.157671e+01
## `myCategory.fctrCulture#Arts#:WordCount.log` 2.157110e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 2.141486e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 2.054855e+01
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 2.049585e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 2.018289e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 1.954478e+01
## `myCategory.fctrCulture#Arts#` 1.942482e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 1.924374e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 1.862809e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 1.856946e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 1.848269e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 1.803981e+01
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 1.668170e+01
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 1.653240e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 1.644645e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 1.641889e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 1.565868e+01
## `myCategory.fctr##:.clusterid.fctr3` 1.540990e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 1.537242e+01
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` 1.504956e+01
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` 1.492789e+01
## `myCategory.fctrScience#Health#:H.npnct19.log` 1.490172e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 1.462603e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 1.460209e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 1.458054e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 1.456803e+01
## `myCategory.fctrForeign#World#Asia Pacific` 1.352566e+01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 1.339390e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 1.319672e+01
## `myCategory.fctr##:.clusterid.fctr18` 1.314696e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 1.314612e+01
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 1.314288e+01
## `myCategory.fctr##:.clusterid.fctr9` 1.314042e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 1.307802e+01
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 1.304453e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 1.295186e+01
## `myCategory.fctr##:.clusterid.fctr12` 1.276039e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 1.271906e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 1.269929e+01
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 1.266601e+01
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 1.189402e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 1.187027e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 1.181831e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 1.126736e+01
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 1.066652e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 1.055727e+01
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 1.054340e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 1.052033e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 1.047047e+01
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 9.917204e+00
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 9.865082e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 9.320117e+00
## `myCategory.fctrScience#Health#:WordCount.log` 9.166309e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 8.993313e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 8.744250e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 8.643218e+00
## `myCategory.fctrTStyle##` 8.451106e+00
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 7.968954e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 7.926461e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 7.289080e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 7.111804e+00
## `myCategory.fctrBusiness#Technology#` 6.909644e+00
## `myCategory.fctrScience#Health#` 6.863985e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 6.618637e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 6.561348e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 6.457157e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 6.397139e+00
## `myCategory.fctrBusiness#Technology#:WordCount.log` 6.393663e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 6.374634e+00
## `myCategory.fctr##:.clusterid.fctr11` 6.349161e+00
## `myCategory.fctr#Opinion#Room For Debate` 6.275751e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 6.259496e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 6.167661e+00
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 6.083262e+00
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 5.777986e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 5.547585e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 5.485891e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 5.321709e+00
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 5.267173e+00
## `myCategory.fctr##:.clusterid.fctr14` 5.165276e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 5.154312e+00
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 4.945465e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 4.901225e+00
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 4.858091e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 4.847880e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 4.575708e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` 4.394841e+00
## `myCategory.fctr##:.clusterid.fctr6` 4.385923e+00
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 3.912793e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 3.726561e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 3.689364e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 3.575217e+00
## `myCategory.fctr#Multimedia#:WordCount.log` 3.307688e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.130503e+00
## `myCategory.fctr##:.clusterid.fctr15` 3.038014e+00
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 2.871034e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 2.857529e+00
## `myCategory.fctr##:.clusterid.fctr8` 2.844186e+00
## `myCategory.fctr##:.clusterid.fctr13` 2.624995e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 2.567929e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 2.521825e+00
## `myCategory.fctr##:.clusterid.fctr19` 2.497297e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 2.412686e+00
## `myCategory.fctr##:.clusterid.fctr7` 2.265546e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 2.260944e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 2.116159e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 2.036630e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 2.034420e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 2.034308e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 1.851233e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 1.843274e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 1.825427e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 1.809042e+00
## `myCategory.fctr##:.clusterid.fctr17` 1.587524e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 1.553186e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 1.396836e+00
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 1.313310e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 9.764945e-01
## `myCategory.fctrTStyle##:WordCount.log` 7.922531e-01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 6.405127e-01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 4.058020e-01
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 3.238308e-01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 2.504980e-01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 2.468260e-01
## `myCategory.fctr##:.clusterid.fctr10` 7.291866e-03
## `myCategory.fctrTravel#Travel#:WordCount.log` 7.039994e-03
## `myCategory.fctr##:.clusterid.fctr4` 5.667737e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 5.166561e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 5.030792e-03
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 4.917465e-03
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 4.645849e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 4.512925e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 4.119993e-03
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 3.425300e-03
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 3.361237e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 3.303799e-03
## `myCategory.fctr##:.clusterid.fctr20` 3.147075e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 3.127465e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 2.799343e-03
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 1.289043e-03
## `myCategory.fctr#Multimedia#` 1.271788e-03
## `myCategory.fctrTStyle##:H.npnct19.log` 8.624905e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 6.970142e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 6.880603e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 6.800621e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 6.729538e-04
## `myCategory.fctr#U.S.#Education:WordCount.log` 6.684324e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 6.670098e-04
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 6.150200e-04
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 6.114696e-04
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 4.945572e-04
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 4.462171e-04
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 4.248833e-04
## `myCategory.fctrForeign#World#:WordCount.log` 4.178432e-04
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 3.201436e-04
## `myCategory.fctrStyles##Fashion` 2.853249e-04
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 2.722640e-04
## `myCategory.fctrmyOther:WordCount.log` 2.523366e-04
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 2.499844e-04
## `myCategory.fctrStyles##Fashion:WordCount.log` 2.378297e-04
## myCategory.fctrmyOther 2.288603e-04
## `myCategory.fctrForeign#World#` 2.278260e-04
## `myCategory.fctr#U.S.#Education` 1.888960e-04
## `myCategory.fctrTravel#Travel#` 1.855409e-04
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 1.168596e-04
## `myCategory.fctrForeign#World#:H.npnct19.log` 8.573706e-05
## `myCategory.fctrmyOther:H.npnct19.log` 7.394921e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 6.095186e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 5.266618e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 5.256863e-05
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 4.863348e-05
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 4.861938e-05
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 4.852911e-05
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 4.850624e-05
## `myCategory.fctrBusiness#Crosswords/Games#` 4.843395e-05
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 4.828695e-05
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` 3.940152e-05
## `myCategory.fctr#Opinion#The Public Editor` 1.230877e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 1.062662e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 8.954267e-06
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 3.828895e-09
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 2.932202e-09
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 1.750250e-09
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 1.711854e-09
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 1.273225e-09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 5.039579e-11
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 4.652878e-11
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 4.360351e-11
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 4.129570e-11
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 4.089742e-11
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 5.416985e-12
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 3.505663e-12
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 2.827267e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 1.265256e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 1.162710e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 4.740783e-13
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 3.504635e-13
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 3.271409e-13
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 3.267741e-13
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 0.000000e+00
## Rank9.Bayes.glm.glm.importance
## `myCategory.fctrOpEd#Opinion#` 1.000000e+02
## WordCount.log 9.884070e+01
## `myCategory.fctrOpEd#Opinion#:WordCount.log` 7.815739e+01
## H.npnct19.log 5.218153e+01
## `myCategory.fctrMetro#N.Y. / Region#` 4.645295e+01
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` 4.408266e+01
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` 4.268518e+01
## `myCategory.fctr##:.clusterid.fctr2` 4.103112e+01
## `myCategory.fctrStyles#U.S.#` 4.000256e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 3.552842e+01
## `myCategory.fctrBusiness#Business Day#Dealbook` 3.380594e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 3.138751e+01
## `myCategory.fctrStyles#U.S.#:WordCount.log` 3.120595e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 3.018292e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 2.920092e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 2.832896e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 2.696428e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 2.549705e+01
## `myCategory.fctr##:.clusterid.fctr16` 2.419378e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 2.363321e+01
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 2.321685e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 2.268658e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 2.259505e+01
## `myCategory.fctr##:.clusterid.fctr5` 2.157671e+01
## `myCategory.fctrCulture#Arts#:WordCount.log` 2.157110e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 2.141486e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 2.054855e+01
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 2.049585e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 2.018289e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 1.954478e+01
## `myCategory.fctrCulture#Arts#` 1.942482e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 1.924374e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 1.862809e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 1.856946e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 1.848269e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 1.803981e+01
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 1.668170e+01
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 1.653240e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 1.644645e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 1.641889e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 1.565868e+01
## `myCategory.fctr##:.clusterid.fctr3` 1.540990e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 1.537242e+01
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` 1.504956e+01
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` 1.492789e+01
## `myCategory.fctrScience#Health#:H.npnct19.log` 1.490172e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 1.462603e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 1.460209e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 1.458054e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 1.456803e+01
## `myCategory.fctrForeign#World#Asia Pacific` 1.352566e+01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 1.339390e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 1.319672e+01
## `myCategory.fctr##:.clusterid.fctr18` 1.314696e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 1.314612e+01
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 1.314288e+01
## `myCategory.fctr##:.clusterid.fctr9` 1.314042e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 1.307802e+01
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 1.304453e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 1.295186e+01
## `myCategory.fctr##:.clusterid.fctr12` 1.276039e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 1.271906e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 1.269929e+01
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 1.266601e+01
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 1.189402e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 1.187027e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 1.181831e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 1.126736e+01
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 1.066652e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 1.055727e+01
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 1.054340e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 1.052033e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 1.047047e+01
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 9.917204e+00
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 9.865082e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 9.320117e+00
## `myCategory.fctrScience#Health#:WordCount.log` 9.166309e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 8.993313e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 8.744250e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 8.643218e+00
## `myCategory.fctrTStyle##` 8.451106e+00
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 7.968954e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 7.926461e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 7.289080e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 7.111804e+00
## `myCategory.fctrBusiness#Technology#` 6.909644e+00
## `myCategory.fctrScience#Health#` 6.863985e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 6.618637e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 6.561348e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 6.457157e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 6.397139e+00
## `myCategory.fctrBusiness#Technology#:WordCount.log` 6.393663e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 6.374634e+00
## `myCategory.fctr##:.clusterid.fctr11` 6.349161e+00
## `myCategory.fctr#Opinion#Room For Debate` 6.275751e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 6.259496e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 6.167661e+00
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 6.083262e+00
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 5.777986e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 5.547585e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 5.485891e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 5.321709e+00
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 5.267173e+00
## `myCategory.fctr##:.clusterid.fctr14` 5.165276e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 5.154312e+00
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 4.945465e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 4.901225e+00
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 4.858091e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 4.847880e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 4.575708e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` 4.394841e+00
## `myCategory.fctr##:.clusterid.fctr6` 4.385923e+00
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 3.912793e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 3.726561e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 3.689364e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 3.575217e+00
## `myCategory.fctr#Multimedia#:WordCount.log` 3.307688e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.130503e+00
## `myCategory.fctr##:.clusterid.fctr15` 3.038014e+00
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 2.871034e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 2.857529e+00
## `myCategory.fctr##:.clusterid.fctr8` 2.844186e+00
## `myCategory.fctr##:.clusterid.fctr13` 2.624995e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 2.567929e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 2.521825e+00
## `myCategory.fctr##:.clusterid.fctr19` 2.497297e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 2.412686e+00
## `myCategory.fctr##:.clusterid.fctr7` 2.265546e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 2.260944e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 2.116159e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 2.036630e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 2.034420e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 2.034308e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 1.851233e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 1.843274e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 1.825427e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 1.809042e+00
## `myCategory.fctr##:.clusterid.fctr17` 1.587524e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 1.553186e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 1.396836e+00
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 1.313310e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 9.764945e-01
## `myCategory.fctrTStyle##:WordCount.log` 7.922531e-01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 6.405127e-01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 4.058020e-01
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 3.238308e-01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 2.504980e-01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 2.468260e-01
## `myCategory.fctr##:.clusterid.fctr10` 7.291866e-03
## `myCategory.fctrTravel#Travel#:WordCount.log` 7.039994e-03
## `myCategory.fctr##:.clusterid.fctr4` 5.667737e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 5.166561e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 5.030792e-03
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 4.917465e-03
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 4.645849e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 4.512925e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 4.119993e-03
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 3.425300e-03
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 3.361237e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 3.303799e-03
## `myCategory.fctr##:.clusterid.fctr20` 3.147075e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 3.127465e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 2.799343e-03
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 1.289043e-03
## `myCategory.fctr#Multimedia#` 1.271788e-03
## `myCategory.fctrTStyle##:H.npnct19.log` 8.624905e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 6.970142e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 6.880603e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 6.800621e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 6.729538e-04
## `myCategory.fctr#U.S.#Education:WordCount.log` 6.684324e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 6.670098e-04
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 6.150200e-04
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 6.114696e-04
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 4.945572e-04
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 4.462171e-04
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 4.248833e-04
## `myCategory.fctrForeign#World#:WordCount.log` 4.178432e-04
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 3.201436e-04
## `myCategory.fctrStyles##Fashion` 2.853249e-04
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 2.722640e-04
## `myCategory.fctrmyOther:WordCount.log` 2.523366e-04
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 2.499844e-04
## `myCategory.fctrStyles##Fashion:WordCount.log` 2.378297e-04
## myCategory.fctrmyOther 2.288603e-04
## `myCategory.fctrForeign#World#` 2.278260e-04
## `myCategory.fctr#U.S.#Education` 1.888960e-04
## `myCategory.fctrTravel#Travel#` 1.855409e-04
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 1.168596e-04
## `myCategory.fctrForeign#World#:H.npnct19.log` 8.573706e-05
## `myCategory.fctrmyOther:H.npnct19.log` 7.394921e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 6.095186e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 5.266618e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 5.256863e-05
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 4.863348e-05
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 4.861938e-05
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 4.852911e-05
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 4.850624e-05
## `myCategory.fctrBusiness#Crosswords/Games#` 4.843395e-05
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 4.828695e-05
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` 3.940152e-05
## `myCategory.fctr#Opinion#The Public Editor` 1.230877e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 1.062662e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 8.954267e-06
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 3.828895e-09
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 2.932202e-09
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 1.750250e-09
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 1.711854e-09
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 1.273225e-09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 5.039579e-11
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 4.652878e-11
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 4.360351e-11
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 4.129570e-11
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 4.089742e-11
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 5.416985e-12
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 3.505663e-12
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 2.827267e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 1.265256e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 1.162710e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 4.740783e-13
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 3.504635e-13
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 3.271409e-13
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 3.267741e-13
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 0.000000e+00# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
featsimp_df <- glb_featsimp_df
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
# plot_vars_df <- subset(glb_feats_df, importance >
# glb_feats_df[glb_feats_df$id == ".rnorm", "importance"])
plot_vars_df <- orderBy(~ -importance, glb_feats_df)
if (nrow(plot_vars_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(plot_vars_df) > 1, plot_vars_df$id[2],
".rownames"),
feat_y=plot_vars_df$id[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_vars)
# + facet_wrap(reformulate(plot_vars_df$id[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(featsimp_df) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_vars,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
prob_threshold=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## Warning in glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id =
## glb_sel_mdl_id, : Limiting important feature scatter plots to 5 out of 11
## [1] "Min/Max Boundaries: "
## UniqueID Popular.fctr Popular.fctr.predict.Rank9.Bayes.glm.glm.prob
## 6018 6018 N 8.167269e-06
## 6370 6370 Y 9.691114e-01
## Popular.fctr.predict.Rank9.Bayes.glm.glm
## 6018 N
## 6370 Y
## Popular.fctr.predict.Rank9.Bayes.glm.glm.accurate
## 6018 TRUE
## 6370 TRUE
## Popular.fctr.predict.Rank9.Bayes.glm.glm.error .label
## 6018 0 6018
## 6370 0 6370
## [1] "Inaccurate: "
## UniqueID Popular.fctr Popular.fctr.predict.Rank9.Bayes.glm.glm.prob
## 5058 5058 Y 9.112778e-13
## 172 172 Y 2.010486e-12
## 2527 2527 Y 2.105028e-12
## 5486 5486 Y 2.900701e-12
## 1928 1928 Y 1.355857e-11
## 4 4 Y 5.573917e-11
## Popular.fctr.predict.Rank9.Bayes.glm.glm
## 5058 N
## 172 N
## 2527 N
## 5486 N
## 1928 N
## 4 N
## Popular.fctr.predict.Rank9.Bayes.glm.glm.accurate
## 5058 FALSE
## 172 FALSE
## 2527 FALSE
## 5486 FALSE
## 1928 FALSE
## 4 FALSE
## Popular.fctr.predict.Rank9.Bayes.glm.glm.error
## 5058 -0.5
## 172 -0.5
## 2527 -0.5
## 5486 -0.5
## 1928 -0.5
## 4 -0.5
## UniqueID Popular.fctr Popular.fctr.predict.Rank9.Bayes.glm.glm.prob
## 825 825 Y 0.2172142
## 2817 2817 Y 0.2226241
## 2060 2060 Y 0.2310110
## 3188 3188 Y 0.2653508
## 1355 1355 N 0.6583253
## 6055 6055 N 0.8190716
## Popular.fctr.predict.Rank9.Bayes.glm.glm
## 825 N
## 2817 N
## 2060 N
## 3188 N
## 1355 Y
## 6055 Y
## Popular.fctr.predict.Rank9.Bayes.glm.glm.accurate
## 825 FALSE
## 2817 FALSE
## 2060 FALSE
## 3188 FALSE
## 1355 FALSE
## 6055 FALSE
## Popular.fctr.predict.Rank9.Bayes.glm.glm.error
## 825 -0.2827858
## 2817 -0.2773759
## 2060 -0.2689890
## 3188 -0.2346492
## 1355 0.1583253
## 6055 0.3190716
## UniqueID Popular.fctr Popular.fctr.predict.Rank9.Bayes.glm.glm.prob
## 3258 3258 N 0.9561169
## 3025 3025 N 1.0000000
## 5523 5523 N 1.0000000
## 3875 3875 N 1.0000000
## 4377 4377 N 1.0000000
## 1639 1639 N 1.0000000
## Popular.fctr.predict.Rank9.Bayes.glm.glm
## 3258 Y
## 3025 Y
## 5523 Y
## 3875 Y
## 4377 Y
## 1639 Y
## Popular.fctr.predict.Rank9.Bayes.glm.glm.accurate
## 3258 FALSE
## 3025 FALSE
## 5523 FALSE
## 3875 FALSE
## 4377 FALSE
## 1639 FALSE
## Popular.fctr.predict.Rank9.Bayes.glm.glm.error
## 3258 0.4561169
## 3025 0.5000000
## 5523 0.5000000
## 3875 0.5000000
## 4377 0.5000000
## 1639 0.5000000
# gather predictions from models better than MFO.*
#mdl_id <- "Conditional.X.rf"
#mdl_id <- "Conditional.X.cp.0.rpart"
#mdl_id <- "Conditional.X.rpart"
# glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id,
# glb_rsp_var_out)
# print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, mdl_id)],
# glb_OOBobs_df[, glb_rsp_var])$table))
FN_OOB_ids <- c(4721, 4020, 693, 92)
print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])## [1] Popular.fctr
## [2] Popular.fctr.predict.Rank9.Bayes.glm.glm.prob
## [3] Popular.fctr.predict.Rank9.Bayes.glm.glm
## [4] Popular.fctr.predict.Rank9.Bayes.glm.glm.accurate
## <0 rows> (or 0-length row.names)print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
glb_feats_df$id[1:5]])## [1] Popular WordCount.log A.ratio.sum.TfIdf.nwrds
## [4] S.ratio.sum.TfIdf.nwrds WordCount
## <0 rows> (or 0-length row.names)print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
glb_txt_vars])## [1] Headline Snippet Abstract
## <0 rows> (or 0-length row.names)write.csv(glb_OOBobs_df[, c("UniqueID",
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBent.csv"), row.names=FALSE)
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# dsp_tbl(Headline.contains="[Ee]bola")
# sum(sel_obs(Headline.contains="[Ee]bola"))
# ftable(xtabs(Popular ~ NewsDesk.fctr, data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,]))
# xtabs(NewsDesk ~ Popular, #Popular ~ NewsDesk.fctr,
# data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# exclude=NULL)
# print(mycreate_xtab_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# tbl_col_names=c("Popular", "NewsDesk")))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 12 fit.models 7 2 1292.731 1315.104 22.373
## 13 fit.models 7 3 1315.104 NA NAprint(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "PubDate.year.fctr" "H.npnct03.log" "H.npnct09.log"
## [4] "H.npnct10.log" "H.npnct17.log" "H.npnct18.log"
## [7] "H.npnct20.log" "H.npnct21.log" "H.npnct22.log"
## [10] "H.npnct23.log" "H.npnct24.log" "H.npnct25.log"
## [13] "H.npnct26.log" "H.npnct27.log" "H.npnct29.log"
## [16] "H.npnct30.log" "H.P.http" "S.npnct02.log"
## [19] "S.npnct05.log" "S.npnct09.log" "S.npnct10.log"
## [22] "S.npnct17.log" "S.npnct18.log" "S.npnct21.log"
## [25] "S.npnct22.log" "S.npnct23.log" "S.npnct24.log"
## [28] "S.npnct25.log" "S.npnct26.log" "S.npnct27.log"
## [31] "S.npnct29.log" "S.npnct30.log" "S.P.http"
## [34] "A.npnct05.log" "A.npnct09.log" "A.npnct10.log"
## [37] "A.npnct21.log" "A.npnct22.log" "A.npnct23.log"
## [40] "A.npnct24.log" "A.npnct25.log" "A.npnct26.log"
## [43] "A.npnct27.log" "A.npnct29.log" "A.npnct30.log"print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## [1] "PubDate.year.fctr" "H.npnct03.log" "H.npnct09.log"
## [4] "H.npnct10.log" "H.npnct17.log" "H.npnct18.log"
## [7] "H.npnct20.log" "H.npnct21.log" "H.npnct22.log"
## [10] "H.npnct23.log" "H.npnct24.log" "H.npnct25.log"
## [13] "H.npnct26.log" "H.npnct27.log" "H.npnct29.log"
## [16] "H.npnct30.log" "H.P.http" "S.npnct02.log"
## [19] "S.npnct05.log" "S.npnct09.log" "S.npnct10.log"
## [22] "S.npnct17.log" "S.npnct18.log" "S.npnct21.log"
## [25] "S.npnct22.log" "S.npnct23.log" "S.npnct24.log"
## [28] "S.npnct25.log" "S.npnct26.log" "S.npnct27.log"
## [31] "S.npnct29.log" "S.npnct30.log" "S.P.http"
## [34] "A.npnct05.log" "A.npnct09.log" "A.npnct10.log"
## [37] "A.npnct21.log" "A.npnct22.log" "A.npnct23.log"
## [40] "A.npnct24.log" "A.npnct25.log" "A.npnct26.log"
## [43] "A.npnct27.log" "A.npnct29.log" "A.npnct30.log"print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## [1] "PubDate.year.fctr"
## [2] "H.npnct03.log"
## [3] "H.npnct09.log"
## [4] "H.npnct10.log"
## [5] "H.npnct17.log"
## [6] "H.npnct18.log"
## [7] "H.npnct20.log"
## [8] "H.npnct21.log"
## [9] "H.npnct22.log"
## [10] "H.npnct23.log"
## [11] "H.npnct24.log"
## [12] "H.npnct25.log"
## [13] "H.npnct26.log"
## [14] "H.npnct27.log"
## [15] "H.npnct29.log"
## [16] "H.npnct30.log"
## [17] "H.P.http"
## [18] "S.npnct02.log"
## [19] "S.npnct05.log"
## [20] "S.npnct09.log"
## [21] "S.npnct10.log"
## [22] "S.npnct17.log"
## [23] "S.npnct18.log"
## [24] "S.npnct21.log"
## [25] "S.npnct22.log"
## [26] "S.npnct23.log"
## [27] "S.npnct24.log"
## [28] "S.npnct25.log"
## [29] "S.npnct26.log"
## [30] "S.npnct27.log"
## [31] "S.npnct29.log"
## [32] "S.npnct30.log"
## [33] "S.P.http"
## [34] "A.npnct05.log"
## [35] "A.npnct09.log"
## [36] "A.npnct10.log"
## [37] "A.npnct21.log"
## [38] "A.npnct22.log"
## [39] "A.npnct23.log"
## [40] "A.npnct24.log"
## [41] "A.npnct25.log"
## [42] "A.npnct26.log"
## [43] "A.npnct27.log"
## [44] "A.npnct29.log"
## [45] "A.npnct30.log"
## [46] "Popular.fctr.predict.Rank9.Bayes.glm.glm.prob"
## [47] "Popular.fctr.predict.Rank9.Bayes.glm.glm"
## [48] "Popular.fctr.predict.Rank9.Bayes.glm.glm.accurate"for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models 7 3 1315.104 1321.857 6.753
## 14 fit.data.training 8 0 1321.857 NA NAStep 8.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
# To create specific models
# glb_fin_mdl_id <- NULL; glb_fin_mdl <- NULL;
# glb_sel_mdl_id <- "Conditional.X.cp.0.rpart";
# glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]]; print(glb_sel_mdl)
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
# print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl,
# entity_df=glb_fitobs_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
require(gdata)
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=trim(unlist(strsplit(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"feats"], "[,]"))),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnobs_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size## [1] "fitting model: Final.glm"
## [1] " indep_vars: myCategory.fctr*WordCount.log, myCategory.fctr:.clusterid.fctr, myCategory.fctr*H.npnct19.log, myCategory.fctr:PubDate.day.minutes.poly.1, myCategory.fctr:PubDate.day.minutes.poly.2, myCategory.fctr:PubDate.day.minutes.poly.3, myCategory.fctr:PubDate.day.minutes.poly.4, myCategory.fctr:PubDate.day.minutes.poly.5"
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: not plotting observations with leverage one:
## 3693
## Warning: not plotting observations with leverage one:
## 3693
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -8.4904 -0.1717 0.0000 0.0000 8.4904
##
## Coefficients: (314 not defined because of singularities)
## Estimate
## (Intercept) -1.324e+01
## `myCategory.fctr#Multimedia#` -4.925e+15
## `myCategory.fctr#Opinion#Room For Debate` 5.865e+00
## `myCategory.fctr#Opinion#The Public Editor` 5.074e+05
## `myCategory.fctr#U.S.#Education` 2.326e+13
## `myCategory.fctrBusiness#Business Day#Dealbook` -9.951e+00
## `myCategory.fctrBusiness#Business Day#Small Business` -1.205e+16
## `myCategory.fctrBusiness#Crosswords/Games#` -1.203e+17
## `myCategory.fctrBusiness#Technology#` -8.133e+15
## `myCategory.fctrCulture#Arts#` 5.770e+15
## `myCategory.fctrForeign#World#` -6.798e+00
## `myCategory.fctrForeign#World#Asia Pacific` -4.185e+15
## `myCategory.fctrMetro#N.Y. / Region#` 8.313e+00
## myCategory.fctrmyOther 2.435e+15
## `myCategory.fctrOpEd#Opinion#` 1.288e+01
## `myCategory.fctrScience#Health#` 2.694e-01
## `myCategory.fctrStyles##Fashion` -1.401e+15
## `myCategory.fctrStyles#U.S.#` 1.121e+01
## `myCategory.fctrTravel#Travel#` 1.107e+03
## `myCategory.fctrTStyle##` -4.437e+15
## WordCount.log 1.750e+00
## H.npnct19.log 1.671e+00
## `myCategory.fctr#Multimedia#:WordCount.log` 3.084e+14
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` -3.268e+00
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` -1.045e+02
## `myCategory.fctr#U.S.#Education:WordCount.log` -5.631e+14
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 1.319e+00
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` -3.080e+14
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 5.371e+14
## `myCategory.fctrBusiness#Technology#:WordCount.log` 1.152e+15
## `myCategory.fctrCulture#Arts#:WordCount.log` -1.313e+15
## `myCategory.fctrForeign#World#:WordCount.log` -2.579e+00
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` 1.479e+14
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` -1.280e+00
## `myCategory.fctrmyOther:WordCount.log` -7.777e+14
## `myCategory.fctrOpEd#Opinion#:WordCount.log` -1.424e+00
## `myCategory.fctrScience#Health#:WordCount.log` 5.674e-01
## `myCategory.fctrStyles##Fashion:WordCount.log` -4.118e+14
## `myCategory.fctrStyles#U.S.#:WordCount.log` -1.405e+00
## `myCategory.fctrTravel#Travel#:WordCount.log` 8.322e+01
## `myCategory.fctrTStyle##:WordCount.log` 4.341e+14
## `myCategory.fctr##:.clusterid.fctr2` -3.971e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr##:.clusterid.fctr18` -1.275e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr18` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr18` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18` NA
## `myCategory.fctrmyOther:.clusterid.fctr18` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr18` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18` NA
## `myCategory.fctrTStyle##:.clusterid.fctr18` NA
## `myCategory.fctr##:.clusterid.fctr19` -3.723e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19` NA
## `myCategory.fctrmyOther:.clusterid.fctr19` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr19` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
## `myCategory.fctrTStyle##:.clusterid.fctr19` NA
## `myCategory.fctr##:.clusterid.fctr20` -1.503e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20` NA
## `myCategory.fctrmyOther:.clusterid.fctr20` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr20` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
## `myCategory.fctr#Multimedia#:H.npnct19.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` -3.353e+01
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 2.143e+04
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 1.018e+14
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` -2.228e+00
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` -6.017e+14
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` -3.136e+15
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 2.154e+15
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 2.012e+15
## `myCategory.fctrForeign#World#:H.npnct19.log` 1.350e+00
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` -1.726e+15
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 1.580e+00
## `myCategory.fctrmyOther:H.npnct19.log` -7.715e+14
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` -1.441e+00
## `myCategory.fctrScience#Health#:H.npnct19.log` -1.202e+00
## `myCategory.fctrStyles##Fashion:H.npnct19.log` -5.184e+14
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` -1.562e-01
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 3.052e+01
## `myCategory.fctrTStyle##:H.npnct19.log` -1.309e+15
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 3.682e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 7.074e+16
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` -4.811e+02
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` -1.310e+08
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 1.446e+17
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 3.730e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` -4.087e+18
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 2.226e+19
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 4.428e+15
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` -1.488e+15
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` -6.773e+02
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` -6.395e+16
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 4.373e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` -1.528e+18
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 1.023e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` -1.044e+02
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` -6.049e+16
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 2.189e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` -3.908e+05
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 1.282e+17
## `myCategory.fctr##:PubDate.day.minutes.poly.2` -1.009e+02
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` -4.182e+16
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` -1.187e+03
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 1.259e+08
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 1.879e+17
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 1.434e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` -1.531e+18
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` -2.738e+19
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` -2.639e+15
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 3.822e+15
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 9.500e+02
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 1.177e+17
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` -5.771e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 7.758e+17
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` -4.282e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 6.484e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 6.587e+16
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` -1.049e+02
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 4.954e+05
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` -2.830e+17
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 5.660e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 2.034e+16
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` -3.306e+03
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` -1.347e+08
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 1.473e+17
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` -1.140e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` -4.149e+18
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 2.190e+19
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` -3.962e+15
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 3.866e+16
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` -7.105e+02
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 5.403e+16
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` -2.036e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` -2.060e+18
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 7.422e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 5.299e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 4.992e+16
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` -2.500e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` -4.891e+05
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 1.941e+17
## `myCategory.fctr##:PubDate.day.minutes.poly.4` -6.748e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` -7.222e+16
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 6.441e+02
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 4.490e+07
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 1.555e+17
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 3.360e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` -8.672e+17
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` -9.978e+18
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` -9.426e+15
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` -2.745e+16
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 4.667e+02
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` -7.586e+16
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 4.324e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 4.100e+17
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` -4.927e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` -7.416e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` -5.201e+16
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` -8.356e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 2.335e+05
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` -1.722e+17
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 1.973e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 6.378e+16
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` -1.659e+03
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` -2.460e+07
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 8.980e+16
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` -2.388e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` -7.596e+17
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 2.262e+18
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 6.802e+15
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 1.922e+16
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` -2.455e+02
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` -5.415e+16
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` -3.087e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` -7.024e+17
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 3.090e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 3.457e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` -6.489e+16
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` -1.867e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` -1.075e+05
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 4.149e+16
## Std. Error
## (Intercept) 1.312e+00
## `myCategory.fctr#Multimedia#` 7.954e+07
## `myCategory.fctr#Opinion#Room For Debate` 2.226e+01
## `myCategory.fctr#Opinion#The Public Editor` 3.097e+07
## `myCategory.fctr#U.S.#Education` 6.472e+07
## `myCategory.fctrBusiness#Business Day#Dealbook` 2.868e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 2.472e+08
## `myCategory.fctrBusiness#Crosswords/Games#` 3.876e+09
## `myCategory.fctrBusiness#Technology#` 4.409e+07
## `myCategory.fctrCulture#Arts#` 2.369e+07
## `myCategory.fctrForeign#World#` 5.386e+05
## `myCategory.fctrForeign#World#Asia Pacific` 5.536e+07
## `myCategory.fctrMetro#N.Y. / Region#` 2.241e+00
## myCategory.fctrmyOther 1.014e+08
## `myCategory.fctrOpEd#Opinion#` 1.448e+00
## `myCategory.fctrScience#Health#` 2.447e+00
## `myCategory.fctrStyles##Fashion` 9.501e+07
## `myCategory.fctrStyles#U.S.#` 2.024e+00
## `myCategory.fctrTravel#Travel#` 1.048e+06
## `myCategory.fctrTStyle##` 1.676e+07
## WordCount.log 1.900e-01
## H.npnct19.log 3.952e-01
## `myCategory.fctr#Multimedia#:WordCount.log` 9.506e+06
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` 8.543e-01
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` 6.540e+03
## `myCategory.fctr#U.S.#Education:WordCount.log` 5.659e+06
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 4.101e-01
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` 1.924e+07
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 1.125e+07
## `myCategory.fctrBusiness#Technology#:WordCount.log` 7.473e+06
## `myCategory.fctrCulture#Arts#:WordCount.log` 4.121e+06
## `myCategory.fctrForeign#World#:WordCount.log` 3.606e+04
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` 8.274e+06
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` 3.620e-01
## `myCategory.fctrmyOther:WordCount.log` 1.213e+07
## `myCategory.fctrOpEd#Opinion#:WordCount.log` 2.152e-01
## `myCategory.fctrScience#Health#:WordCount.log` 3.868e-01
## `myCategory.fctrStyles##Fashion:WordCount.log` 1.526e+07
## `myCategory.fctrStyles#U.S.#:WordCount.log` 2.857e-01
## `myCategory.fctrTravel#Travel#:WordCount.log` 5.762e+04
## `myCategory.fctrTStyle##:WordCount.log` 2.133e+06
## `myCategory.fctr##:.clusterid.fctr2` 1.056e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 4.719e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 1.304e+07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 8.720e+06
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 3.435e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 5.605e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 4.162e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 4.770e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 5.129e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 1.331e+07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 1.022e+07
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 3.387e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 5.714e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 4.693e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 3.682e+04
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
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## `myCategory.fctrForeign#World#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19` NA
## `myCategory.fctrmyOther:.clusterid.fctr19` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr19` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
## `myCategory.fctrTStyle##:.clusterid.fctr19` NA
## `myCategory.fctr##:.clusterid.fctr20` 1.103e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20` NA
## `myCategory.fctrmyOther:.clusterid.fctr20` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr20` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
## `myCategory.fctr#Multimedia#:H.npnct19.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 1.144e+05
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 1.564e+06
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 1.707e+07
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 2.716e+00
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 2.478e+07
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` 9.879e+07
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 3.810e+07
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 2.061e+07
## `myCategory.fctrForeign#World#:H.npnct19.log` 9.392e+04
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 6.946e+07
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 1.341e+00
## `myCategory.fctrmyOther:H.npnct19.log` 5.412e+07
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 7.440e-01
## `myCategory.fctrScience#Health#:H.npnct19.log` 9.542e-01
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 3.579e+07
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 8.068e-01
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 2.645e+05
## `myCategory.fctrTStyle##:H.npnct19.log` 5.632e+07
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 6.888e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 8.753e+09
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 1.540e+03
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 8.123e+09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 1.032e+10
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 1.597e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 1.589e+11
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 7.528e+11
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 3.256e+08
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 3.985e+08
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 3.214e+07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 4.942e+08
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 4.121e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 2.487e+10
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 2.099e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 4.749e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 5.565e+08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 9.406e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 2.256e+08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 3.163e+09
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 8.092e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 6.021e+09
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 1.910e+03
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 7.722e+09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 1.214e+10
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 1.502e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 7.088e+10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 9.472e+11
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 3.406e+08
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 3.215e+08
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 5.626e+07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 7.515e+08
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 5.105e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 1.173e+10
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 2.743e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 2.984e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 6.391e+08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 1.188e+02
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 2.766e+08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 3.730e+09
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 9.514e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 1.033e+10
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 4.478e+03
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 8.382e+09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 1.029e+10
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 1.404e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 1.906e+11
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 7.733e+11
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 3.060e+08
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 3.012e+08
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 3.722e+07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 4.889e+08
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 4.869e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 3.229e+10
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 3.726e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 2.761e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 6.974e+08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 1.162e+02
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 2.798e+08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 4.398e+09
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 4.649e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 5.245e+09
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 1.602e+03
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 2.759e+09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 6.479e+09
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 1.710e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 3.150e+10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 3.615e+11
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 3.544e+08
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 3.342e+08
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 1.521e+07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 4.686e+08
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 3.672e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 7.471e+09
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 2.932e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 3.794e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 7.654e+08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 6.150e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 1.268e+08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 2.044e+09
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 3.864e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 5.568e+09
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 2.183e+03
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 1.547e+09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 2.343e+09
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 1.545e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 5.275e+10
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 9.936e+10
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 3.344e+08
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 3.083e+08
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 2.639e+07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 5.007e+08
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.268e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 1.200e+10
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 2.082e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 3.356e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 6.073e+08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 5.436e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 6.072e+07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 1.516e+09
## z value
## (Intercept) -1.010e+01
## `myCategory.fctr#Multimedia#` -6.192e+07
## `myCategory.fctr#Opinion#Room For Debate` 2.630e-01
## `myCategory.fctr#Opinion#The Public Editor` 1.600e-02
## `myCategory.fctr#U.S.#Education` 3.594e+05
## `myCategory.fctrBusiness#Business Day#Dealbook` -3.470e+00
## `myCategory.fctrBusiness#Business Day#Small Business` -4.875e+07
## `myCategory.fctrBusiness#Crosswords/Games#` -3.103e+07
## `myCategory.fctrBusiness#Technology#` -1.845e+08
## `myCategory.fctrCulture#Arts#` 2.435e+08
## `myCategory.fctrForeign#World#` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific` -7.560e+07
## `myCategory.fctrMetro#N.Y. / Region#` 3.709e+00
## myCategory.fctrmyOther 2.402e+07
## `myCategory.fctrOpEd#Opinion#` 8.896e+00
## `myCategory.fctrScience#Health#` 1.100e-01
## `myCategory.fctrStyles##Fashion` -1.475e+07
## `myCategory.fctrStyles#U.S.#` 5.540e+00
## `myCategory.fctrTravel#Travel#` 1.000e-03
## `myCategory.fctrTStyle##` -2.648e+08
## WordCount.log 9.210e+00
## H.npnct19.log 4.230e+00
## `myCategory.fctr#Multimedia#:WordCount.log` 3.245e+07
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` -3.825e+00
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` -1.600e-02
## `myCategory.fctr#U.S.#Education:WordCount.log` -9.950e+07
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 3.217e+00
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` -1.601e+07
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 4.775e+07
## `myCategory.fctrBusiness#Technology#:WordCount.log` 1.541e+08
## `myCategory.fctrCulture#Arts#:WordCount.log` -3.186e+08
## `myCategory.fctrForeign#World#:WordCount.log` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` 1.787e+07
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` -3.536e+00
## `myCategory.fctrmyOther:WordCount.log` -6.412e+07
## `myCategory.fctrOpEd#Opinion#:WordCount.log` -6.617e+00
## `myCategory.fctrScience#Health#:WordCount.log` 1.467e+00
## `myCategory.fctrStyles##Fashion:WordCount.log` -2.698e+07
## `myCategory.fctrStyles#U.S.#:WordCount.log` -4.917e+00
## `myCategory.fctrTravel#Travel#:WordCount.log` 1.000e-03
## `myCategory.fctrTStyle##:WordCount.log` 2.035e+08
## `myCategory.fctr##:.clusterid.fctr2` -3.762e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` -2.630e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 9.118e+07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` -9.360e+07
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` -2.094e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr2` -1.233e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` -1.361e+00
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` -9.290e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 3.150e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 1.902e+07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 1.358e+08
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` -2.084e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr3` -4.330e-01
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` -7.670e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` -1.000e-03
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 1.610e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 6.294e+07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 1.497e+08
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` -4.800e-01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` -1.069e+00
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` -6.910e-01
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` -1.517e+00
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 1.697e+00
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 2.931e+07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` -4.110e+07
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` -1.598e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` -7.560e-01
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` -1.430e-01
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` -9.340e+07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` -4.915e+07
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## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` -1.937e+00
## `myCategory.fctrScience#Health#:H.npnct19.log` -1.260e+00
## `myCategory.fctrStyles##Fashion:H.npnct19.log` -1.449e+07
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` -1.940e-01
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 0.000e+00
## `myCategory.fctrTStyle##:H.npnct19.log` -2.325e+07
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 5.350e-01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 8.082e+06
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` -3.120e-01
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` -1.600e-02
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 1.401e+07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 2.335e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` -2.572e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 2.957e+07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 1.360e+07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` -3.734e+06
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` -1.294e+08
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 1.061e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` -6.144e+07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 4.870e-01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` -2.199e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` -1.087e+08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 2.330e-01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` -2.000e-03
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 4.053e+07
## `myCategory.fctr##:PubDate.day.minutes.poly.2` -1.247e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` -6.945e+06
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` -6.210e-01
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 1.600e-02
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 1.548e+07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 9.550e-01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` -2.160e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` -2.890e+07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` -7.748e+06
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 1.189e+07
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 1.566e+08
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` -1.130e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 6.616e+07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` -1.561e+00
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 2.173e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 1.031e+08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` -8.830e-01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 2.000e-03
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` -7.588e+07
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 5.950e-01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 1.970e+06
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` -7.380e-01
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` -1.600e-02
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 1.431e+07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` -8.120e-01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` -2.177e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 2.833e+07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` -1.295e+07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 1.284e+08
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 1.105e+08
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` -4.180e-01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` -6.380e+07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 1.992e+00
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 1.920e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 7.158e+07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` -2.150e-01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` -2.000e-03
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 4.414e+07
## `myCategory.fctr##:PubDate.day.minutes.poly.4` -1.452e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` -1.377e+07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 4.020e-01
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 1.600e-02
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 2.400e+07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 1.965e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` -2.753e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` -2.760e+07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` -2.659e+07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` -8.213e+07
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` -1.619e+08
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 1.178e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 5.488e+07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` -1.680e+00
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` -1.955e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` -6.795e+07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` -1.359e+00
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 2.000e-03
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` -8.425e+07
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 5.100e-02
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 1.146e+07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` -7.600e-01
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` -1.600e-02
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 3.833e+07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` -1.546e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` -1.440e+07
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 2.276e+07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 2.034e+07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 6.234e+07
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 0.000e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` -1.082e+08
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` -9.440e-01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` -5.855e+07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 1.484e+00
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 1.030e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` -1.069e+08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` -3.440e-01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` -2.000e-03
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 2.737e+07
## Pr(>|z|)
## (Intercept) < 2e-16
## `myCategory.fctr#Multimedia#` < 2e-16
## `myCategory.fctr#Opinion#Room For Debate` 0.792169
## `myCategory.fctr#Opinion#The Public Editor` 0.986927
## `myCategory.fctr#U.S.#Education` < 2e-16
## `myCategory.fctrBusiness#Business Day#Dealbook` 0.000521
## `myCategory.fctrBusiness#Business Day#Small Business` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#` < 2e-16
## `myCategory.fctrBusiness#Technology#` < 2e-16
## `myCategory.fctrCulture#Arts#` < 2e-16
## `myCategory.fctrForeign#World#` 0.999990
## `myCategory.fctrForeign#World#Asia Pacific` < 2e-16
## `myCategory.fctrMetro#N.Y. / Region#` 0.000208
## myCategory.fctrmyOther < 2e-16
## `myCategory.fctrOpEd#Opinion#` < 2e-16
## `myCategory.fctrScience#Health#` 0.912314
## `myCategory.fctrStyles##Fashion` < 2e-16
## `myCategory.fctrStyles#U.S.#` 3.03e-08
## `myCategory.fctrTravel#Travel#` 0.999157
## `myCategory.fctrTStyle##` < 2e-16
## WordCount.log < 2e-16
## H.npnct19.log 2.34e-05
## `myCategory.fctr#Multimedia#:WordCount.log` < 2e-16
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` 0.000131
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` 0.987250
## `myCategory.fctr#U.S.#Education:WordCount.log` < 2e-16
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 0.001294
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` < 2e-16
## `myCategory.fctrBusiness#Technology#:WordCount.log` < 2e-16
## `myCategory.fctrCulture#Arts#:WordCount.log` < 2e-16
## `myCategory.fctrForeign#World#:WordCount.log` 0.999943
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` < 2e-16
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` 0.000406
## `myCategory.fctrmyOther:WordCount.log` < 2e-16
## `myCategory.fctrOpEd#Opinion#:WordCount.log` 3.66e-11
## `myCategory.fctrScience#Health#:WordCount.log` 0.142428
## `myCategory.fctrStyles##Fashion:WordCount.log` < 2e-16
## `myCategory.fctrStyles#U.S.#:WordCount.log` 8.77e-07
## `myCategory.fctrTravel#Travel#:WordCount.log` 0.998848
## `myCategory.fctrTStyle##:WordCount.log` < 2e-16
## `myCategory.fctr##:.clusterid.fctr2` 0.000169
## `myCategory.fctr#Multimedia#:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr2` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr2` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 0.792340
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` < 2e-16
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` < 2e-16
## `myCategory.fctrForeign#World#:.clusterid.fctr2` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2` NA
## `myCategory.fctrmyOther:.clusterid.fctr2` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 0.036295
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 0.217678
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 0.173482
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2` NA
## `myCategory.fctrTStyle##:.clusterid.fctr2` NA
## `myCategory.fctr##:.clusterid.fctr3` 0.352646
## `myCategory.fctr#Multimedia#:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr3` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr3` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 0.752805
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` < 2e-16
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` < 2e-16
## `myCategory.fctrForeign#World#:.clusterid.fctr3` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3` NA
## `myCategory.fctrmyOther:.clusterid.fctr3` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 0.037150
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 0.664960
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 0.443156
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3` NA
## `myCategory.fctrTStyle##:.clusterid.fctr3` NA
## `myCategory.fctr##:.clusterid.fctr4` 0.999505
## `myCategory.fctr#Multimedia#:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 0.871822
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` < 2e-16
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` < 2e-16
## `myCategory.fctrForeign#World#:.clusterid.fctr4` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4` NA
## `myCategory.fctrmyOther:.clusterid.fctr4` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 0.631444
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 0.284971
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 0.489678
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4` NA
## `myCategory.fctrTStyle##:.clusterid.fctr4` NA
## `myCategory.fctr##:.clusterid.fctr5` 0.129183
## `myCategory.fctr#Multimedia#:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 0.089655
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` < 2e-16
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` < 2e-16
## `myCategory.fctrForeign#World#:.clusterid.fctr5` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5` NA
## `myCategory.fctrmyOther:.clusterid.fctr5` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 0.110087
## `myCategory.fctrScience#Health#:.clusterid.fctr5` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5` NA
## `myCategory.fctrTStyle##:.clusterid.fctr5` NA
## `myCategory.fctr##:.clusterid.fctr6` 0.449467
## `myCategory.fctr#Multimedia#:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 0.886453
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` < 2e-16
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` < 2e-16
## `myCategory.fctrForeign#World#:.clusterid.fctr6` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6` NA
## `myCategory.fctrmyOther:.clusterid.fctr6` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 0.474109
## `myCategory.fctrScience#Health#:.clusterid.fctr6` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6` NA
## `myCategory.fctrTStyle##:.clusterid.fctr6` NA
## `myCategory.fctr##:.clusterid.fctr7` 0.430884
## `myCategory.fctr#Multimedia#:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 0.597460
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` < 2e-16
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` < 2e-16
## `myCategory.fctrForeign#World#:.clusterid.fctr7` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7` NA
## `myCategory.fctrmyOther:.clusterid.fctr7` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 0.714125
## `myCategory.fctrScience#Health#:.clusterid.fctr7` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7` NA
## `myCategory.fctrTStyle##:.clusterid.fctr7` NA
## `myCategory.fctr##:.clusterid.fctr8` 0.297581
## `myCategory.fctr#Multimedia#:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr8` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 0.679672
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` < 2e-16
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` < 2e-16
## `myCategory.fctrForeign#World#:.clusterid.fctr8` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8` NA
## `myCategory.fctrmyOther:.clusterid.fctr8` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 0.008230
## `myCategory.fctrScience#Health#:.clusterid.fctr8` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr8` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr8` NA
## `myCategory.fctrTStyle##:.clusterid.fctr8` NA
## `myCategory.fctr##:.clusterid.fctr9` 0.980941
## `myCategory.fctr#Multimedia#:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr9` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr9` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 0.949384
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` < 2e-16
## `myCategory.fctrForeign#World#:.clusterid.fctr9` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9` NA
## `myCategory.fctrmyOther:.clusterid.fctr9` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr9` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr9` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr9` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr9` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr9` NA
## `myCategory.fctrTStyle##:.clusterid.fctr9` NA
## `myCategory.fctr##:.clusterid.fctr10` 0.999574
## `myCategory.fctr#Multimedia#:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr10` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr10` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 0.179597
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` < 2e-16
## `myCategory.fctrForeign#World#:.clusterid.fctr10` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10` NA
## `myCategory.fctrmyOther:.clusterid.fctr10` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr10` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr10` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr10` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr10` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr10` NA
## `myCategory.fctrTStyle##:.clusterid.fctr10` NA
## `myCategory.fctr##:.clusterid.fctr11` 0.673974
## `myCategory.fctr#Multimedia#:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr11` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr11` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 0.576669
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr11` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` < 2e-16
## `myCategory.fctrForeign#World#:.clusterid.fctr11` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr11` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr11` NA
## `myCategory.fctrmyOther:.clusterid.fctr11` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr11` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr11` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr11` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr11` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr11` NA
## `myCategory.fctrTStyle##:.clusterid.fctr11` NA
## `myCategory.fctr##:.clusterid.fctr12` 0.105083
## `myCategory.fctr#Multimedia#:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr12` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr12` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 0.856223
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr12` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` < 2e-16
## `myCategory.fctrForeign#World#:.clusterid.fctr12` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr12` NA
## `myCategory.fctrmyOther:.clusterid.fctr12` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr12` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr12` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr12` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12` NA
## `myCategory.fctrTStyle##:.clusterid.fctr12` NA
## `myCategory.fctr##:.clusterid.fctr13` 0.749995
## `myCategory.fctr#Multimedia#:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 0.523061
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` < 2e-16
## `myCategory.fctrForeign#World#:.clusterid.fctr13` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13` NA
## `myCategory.fctrmyOther:.clusterid.fctr13` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr13` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13` NA
## `myCategory.fctrTStyle##:.clusterid.fctr13` NA
## `myCategory.fctr##:.clusterid.fctr14` 0.481351
## `myCategory.fctr#Multimedia#:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 0.999460
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` < 2e-16
## `myCategory.fctrForeign#World#:.clusterid.fctr14` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14` NA
## `myCategory.fctrmyOther:.clusterid.fctr14` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr14` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14` NA
## `myCategory.fctrTStyle##:.clusterid.fctr14` NA
## `myCategory.fctr##:.clusterid.fctr15` 0.940793
## `myCategory.fctr#Multimedia#:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 0.703653
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr15` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15` NA
## `myCategory.fctrmyOther:.clusterid.fctr15` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr15` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15` NA
## `myCategory.fctrTStyle##:.clusterid.fctr15` NA
## `myCategory.fctr##:.clusterid.fctr16` 0.031390
## `myCategory.fctr#Multimedia#:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 0.370869
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr16` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16` NA
## `myCategory.fctrmyOther:.clusterid.fctr16` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr16` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16` NA
## `myCategory.fctrTStyle##:.clusterid.fctr16` NA
## `myCategory.fctr##:.clusterid.fctr17` 0.721598
## `myCategory.fctr#Multimedia#:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr17` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17` NA
## `myCategory.fctrmyOther:.clusterid.fctr17` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr17` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17` NA
## `myCategory.fctrTStyle##:.clusterid.fctr17` NA
## `myCategory.fctr##:.clusterid.fctr18` 0.254405
## `myCategory.fctr#Multimedia#:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr18` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr18` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr18` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr18` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr18` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18` NA
## `myCategory.fctrmyOther:.clusterid.fctr18` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr18` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18` NA
## `myCategory.fctrTStyle##:.clusterid.fctr18` NA
## `myCategory.fctr##:.clusterid.fctr19` 0.787014
## `myCategory.fctr#Multimedia#:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr19` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19` NA
## `myCategory.fctrmyOther:.clusterid.fctr19` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr19` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19` NA
## `myCategory.fctrTStyle##:.clusterid.fctr19` NA
## `myCategory.fctr##:.clusterid.fctr20` 0.173124
## `myCategory.fctr#Multimedia#:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20` NA
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20` NA
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20` NA
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20` NA
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#:.clusterid.fctr20` NA
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20` NA
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20` NA
## `myCategory.fctrmyOther:.clusterid.fctr20` NA
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20` NA
## `myCategory.fctrScience#Health#:.clusterid.fctr20` NA
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20` NA
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20` NA
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20` NA
## `myCategory.fctrTStyle##:.clusterid.fctr20` NA
## `myCategory.fctr#Multimedia#:H.npnct19.log` NA
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 0.999766
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 0.989064
## `myCategory.fctr#U.S.#Education:H.npnct19.log` < 2e-16
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 0.412205
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` < 2e-16
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` < 2e-16
## `myCategory.fctrCulture#Arts#:H.npnct19.log` < 2e-16
## `myCategory.fctrForeign#World#:H.npnct19.log` 0.999989
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` < 2e-16
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 0.238675
## `myCategory.fctrmyOther:H.npnct19.log` < 2e-16
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 0.052730
## `myCategory.fctrScience#Health#:H.npnct19.log` 0.207687
## `myCategory.fctrStyles##Fashion:H.npnct19.log` < 2e-16
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 0.846489
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 0.999908
## `myCategory.fctrTStyle##:H.npnct19.log` < 2e-16
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 0.592953
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` < 2e-16
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 0.754738
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 0.987131
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` < 2e-16
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 0.019526
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` < 2e-16
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` < 2e-16
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` < 2e-16
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 0.999983
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` < 2e-16
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 0.288578
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` < 2e-16
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 0.626125
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 0.027875
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` < 2e-16
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 0.816001
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 0.998618
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` < 2e-16
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 0.212295
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` < 2e-16
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 0.534277
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 0.986993
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` < 2e-16
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 0.339742
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` < 2e-16
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` < 2e-16
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` < 2e-16
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 0.999987
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` < 2e-16
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 0.258277
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` < 2e-16
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 0.118437
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 0.029787
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` < 2e-16
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 0.377046
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 0.998571
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` < 2e-16
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 0.551928
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` < 2e-16
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 0.460335
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 0.987176
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` < 2e-16
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 0.416829
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` < 2e-16
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` < 2e-16
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` < 2e-16
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 0.999985
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` < 2e-16
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 0.675773
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` < 2e-16
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 0.046358
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 0.054911
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` < 2e-16
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 0.829693
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 0.998605
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` < 2e-16
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 0.146626
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` < 2e-16
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 0.687656
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 0.987017
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` < 2e-16
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 0.049471
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` < 2e-16
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` < 2e-16
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` < 2e-16
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 0.999976
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` < 2e-16
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 0.238945
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` < 2e-16
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 0.092880
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 0.050602
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` < 2e-16
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 0.174261
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 0.998530
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` < 2e-16
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 0.959287
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` < 2e-16
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 0.447128
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 0.987319
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` < 2e-16
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 0.122167
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` < 2e-16
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` < 2e-16
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` < 2e-16
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` < 2e-16
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 0.999993
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` < 2e-16
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 0.344931
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` < 2e-16
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 0.137821
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 0.302927
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` < 2e-16
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 0.731175
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 0.998587
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` < 2e-16
##
## (Intercept) ***
## `myCategory.fctr#Multimedia#` ***
## `myCategory.fctr#Opinion#Room For Debate`
## `myCategory.fctr#Opinion#The Public Editor`
## `myCategory.fctr#U.S.#Education` ***
## `myCategory.fctrBusiness#Business Day#Dealbook` ***
## `myCategory.fctrBusiness#Business Day#Small Business` ***
## `myCategory.fctrBusiness#Crosswords/Games#` ***
## `myCategory.fctrBusiness#Technology#` ***
## `myCategory.fctrCulture#Arts#` ***
## `myCategory.fctrForeign#World#`
## `myCategory.fctrForeign#World#Asia Pacific` ***
## `myCategory.fctrMetro#N.Y. / Region#` ***
## myCategory.fctrmyOther ***
## `myCategory.fctrOpEd#Opinion#` ***
## `myCategory.fctrScience#Health#`
## `myCategory.fctrStyles##Fashion` ***
## `myCategory.fctrStyles#U.S.#` ***
## `myCategory.fctrTravel#Travel#`
## `myCategory.fctrTStyle##` ***
## WordCount.log ***
## H.npnct19.log ***
## `myCategory.fctr#Multimedia#:WordCount.log` ***
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` ***
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log`
## `myCategory.fctr#U.S.#Education:WordCount.log` ***
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` **
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` ***
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` ***
## `myCategory.fctrBusiness#Technology#:WordCount.log` ***
## `myCategory.fctrCulture#Arts#:WordCount.log` ***
## `myCategory.fctrForeign#World#:WordCount.log`
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` ***
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` ***
## `myCategory.fctrmyOther:WordCount.log` ***
## `myCategory.fctrOpEd#Opinion#:WordCount.log` ***
## `myCategory.fctrScience#Health#:WordCount.log`
## `myCategory.fctrStyles##Fashion:WordCount.log` ***
## `myCategory.fctrStyles#U.S.#:WordCount.log` ***
## `myCategory.fctrTravel#Travel#:WordCount.log`
## `myCategory.fctrTStyle##:WordCount.log` ***
## `myCategory.fctr##:.clusterid.fctr2` ***
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## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr2`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` ***
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` ***
## `myCategory.fctrForeign#World#:.clusterid.fctr2`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr2`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr2`
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## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` *
## `myCategory.fctrScience#Health#:.clusterid.fctr2`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr2`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr2`
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## `myCategory.fctr##:.clusterid.fctr3`
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## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr3`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` ***
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` ***
## `myCategory.fctrForeign#World#:.clusterid.fctr3`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr3`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr3`
## `myCategory.fctrmyOther:.clusterid.fctr3`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` *
## `myCategory.fctrScience#Health#:.clusterid.fctr3`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr3`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr3`
## `myCategory.fctrTStyle##:.clusterid.fctr3`
## `myCategory.fctr##:.clusterid.fctr4`
## `myCategory.fctr#Multimedia#:.clusterid.fctr4`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr4`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr4`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr4`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr4`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr4`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` ***
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` ***
## `myCategory.fctrForeign#World#:.clusterid.fctr4`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr4`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr4`
## `myCategory.fctrmyOther:.clusterid.fctr4`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4`
## `myCategory.fctrScience#Health#:.clusterid.fctr4`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr4`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr4`
## `myCategory.fctrTStyle##:.clusterid.fctr4`
## `myCategory.fctr##:.clusterid.fctr5`
## `myCategory.fctr#Multimedia#:.clusterid.fctr5`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr5`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr5`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr5`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` .
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr5`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr5`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` ***
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` ***
## `myCategory.fctrForeign#World#:.clusterid.fctr5`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr5`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr5`
## `myCategory.fctrmyOther:.clusterid.fctr5`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5`
## `myCategory.fctrScience#Health#:.clusterid.fctr5`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr5`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr5`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr5`
## `myCategory.fctrTStyle##:.clusterid.fctr5`
## `myCategory.fctr##:.clusterid.fctr6`
## `myCategory.fctr#Multimedia#:.clusterid.fctr6`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr6`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr6`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr6`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr6`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr6`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` ***
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` ***
## `myCategory.fctrForeign#World#:.clusterid.fctr6`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr6`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr6`
## `myCategory.fctrmyOther:.clusterid.fctr6`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6`
## `myCategory.fctrScience#Health#:.clusterid.fctr6`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr6`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr6`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr6`
## `myCategory.fctrTStyle##:.clusterid.fctr6`
## `myCategory.fctr##:.clusterid.fctr7`
## `myCategory.fctr#Multimedia#:.clusterid.fctr7`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr7`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr7`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr7`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr7`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr7`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` ***
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` ***
## `myCategory.fctrForeign#World#:.clusterid.fctr7`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr7`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr7`
## `myCategory.fctrmyOther:.clusterid.fctr7`
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## `myCategory.fctrScience#Health#:.clusterid.fctr7`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr7`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr7`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr7`
## `myCategory.fctrTStyle##:.clusterid.fctr7`
## `myCategory.fctr##:.clusterid.fctr8`
## `myCategory.fctr#Multimedia#:.clusterid.fctr8`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr8`
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr8`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr8`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` ***
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` ***
## `myCategory.fctrForeign#World#:.clusterid.fctr8`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr8`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr8`
## `myCategory.fctrmyOther:.clusterid.fctr8`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` **
## `myCategory.fctrScience#Health#:.clusterid.fctr8`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr8`
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## `myCategory.fctr##:.clusterid.fctr9`
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## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr9`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr9`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr9`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` ***
## `myCategory.fctrForeign#World#:.clusterid.fctr9`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr9`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr9`
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## `myCategory.fctr##:.clusterid.fctr10`
## `myCategory.fctr#Multimedia#:.clusterid.fctr10`
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## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr10`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr10`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr10`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` ***
## `myCategory.fctrForeign#World#:.clusterid.fctr10`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr10`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr10`
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## `myCategory.fctrScience#Health#:.clusterid.fctr10`
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## `myCategory.fctrBusiness#Technology#:.clusterid.fctr11`
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## `myCategory.fctrBusiness#Technology#:.clusterid.fctr12`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` ***
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## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr12`
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## `myCategory.fctrStyles##Fashion:.clusterid.fctr12`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr12`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr12`
## `myCategory.fctrTStyle##:.clusterid.fctr12`
## `myCategory.fctr##:.clusterid.fctr13`
## `myCategory.fctr#Multimedia#:.clusterid.fctr13`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr13`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr13`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr13`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr13`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr13`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr13`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` ***
## `myCategory.fctrForeign#World#:.clusterid.fctr13`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr13`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr13`
## `myCategory.fctrmyOther:.clusterid.fctr13`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr13`
## `myCategory.fctrScience#Health#:.clusterid.fctr13`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr13`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr13`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr13`
## `myCategory.fctrTStyle##:.clusterid.fctr13`
## `myCategory.fctr##:.clusterid.fctr14`
## `myCategory.fctr#Multimedia#:.clusterid.fctr14`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr14`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr14`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr14`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr14`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr14`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr14`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` ***
## `myCategory.fctrForeign#World#:.clusterid.fctr14`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr14`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr14`
## `myCategory.fctrmyOther:.clusterid.fctr14`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr14`
## `myCategory.fctrScience#Health#:.clusterid.fctr14`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr14`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr14`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr14`
## `myCategory.fctrTStyle##:.clusterid.fctr14`
## `myCategory.fctr##:.clusterid.fctr15`
## `myCategory.fctr#Multimedia#:.clusterid.fctr15`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr15`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr15`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr15`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr15`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr15`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr15`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr15`
## `myCategory.fctrForeign#World#:.clusterid.fctr15`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr15`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr15`
## `myCategory.fctrmyOther:.clusterid.fctr15`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr15`
## `myCategory.fctrScience#Health#:.clusterid.fctr15`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr15`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr15`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr15`
## `myCategory.fctrTStyle##:.clusterid.fctr15`
## `myCategory.fctr##:.clusterid.fctr16` *
## `myCategory.fctr#Multimedia#:.clusterid.fctr16`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr16`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr16`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr16`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr16`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr16`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr16`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr16`
## `myCategory.fctrForeign#World#:.clusterid.fctr16`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr16`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr16`
## `myCategory.fctrmyOther:.clusterid.fctr16`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr16`
## `myCategory.fctrScience#Health#:.clusterid.fctr16`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr16`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr16`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr16`
## `myCategory.fctrTStyle##:.clusterid.fctr16`
## `myCategory.fctr##:.clusterid.fctr17`
## `myCategory.fctr#Multimedia#:.clusterid.fctr17`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr17`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr17`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr17`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr17`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr17`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr17`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr17`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr17`
## `myCategory.fctrForeign#World#:.clusterid.fctr17`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr17`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr17`
## `myCategory.fctrmyOther:.clusterid.fctr17`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr17`
## `myCategory.fctrScience#Health#:.clusterid.fctr17`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr17`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr17`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr17`
## `myCategory.fctrTStyle##:.clusterid.fctr17`
## `myCategory.fctr##:.clusterid.fctr18`
## `myCategory.fctr#Multimedia#:.clusterid.fctr18`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr18`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr18`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr18`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr18`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr18`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr18`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr18`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr18`
## `myCategory.fctrForeign#World#:.clusterid.fctr18`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr18`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr18`
## `myCategory.fctrmyOther:.clusterid.fctr18`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr18`
## `myCategory.fctrScience#Health#:.clusterid.fctr18`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr18`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr18`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr18`
## `myCategory.fctrTStyle##:.clusterid.fctr18`
## `myCategory.fctr##:.clusterid.fctr19`
## `myCategory.fctr#Multimedia#:.clusterid.fctr19`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr19`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr19`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr19`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr19`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr19`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr19`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr19`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr19`
## `myCategory.fctrForeign#World#:.clusterid.fctr19`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr19`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr19`
## `myCategory.fctrmyOther:.clusterid.fctr19`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr19`
## `myCategory.fctrScience#Health#:.clusterid.fctr19`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr19`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr19`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr19`
## `myCategory.fctrTStyle##:.clusterid.fctr19`
## `myCategory.fctr##:.clusterid.fctr20`
## `myCategory.fctr#Multimedia#:.clusterid.fctr20`
## `myCategory.fctr#Opinion#Room For Debate:.clusterid.fctr20`
## `myCategory.fctr#Opinion#The Public Editor:.clusterid.fctr20`
## `myCategory.fctr#U.S.#Education:.clusterid.fctr20`
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr20`
## `myCategory.fctrBusiness#Business Day#Small Business:.clusterid.fctr20`
## `myCategory.fctrBusiness#Crosswords/Games#:.clusterid.fctr20`
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr20`
## `myCategory.fctrCulture#Arts#:.clusterid.fctr20`
## `myCategory.fctrForeign#World#:.clusterid.fctr20`
## `myCategory.fctrForeign#World#Asia Pacific:.clusterid.fctr20`
## `myCategory.fctrMetro#N.Y. / Region#:.clusterid.fctr20`
## `myCategory.fctrmyOther:.clusterid.fctr20`
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr20`
## `myCategory.fctrScience#Health#:.clusterid.fctr20`
## `myCategory.fctrStyles##Fashion:.clusterid.fctr20`
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr20`
## `myCategory.fctrTravel#Travel#:.clusterid.fctr20`
## `myCategory.fctrTStyle##:.clusterid.fctr20`
## `myCategory.fctr#Multimedia#:H.npnct19.log`
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log`
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log`
## `myCategory.fctr#U.S.#Education:H.npnct19.log` ***
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log`
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` ***
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` ***
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` ***
## `myCategory.fctrCulture#Arts#:H.npnct19.log` ***
## `myCategory.fctrForeign#World#:H.npnct19.log`
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` ***
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log`
## `myCategory.fctrmyOther:H.npnct19.log` ***
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` .
## `myCategory.fctrScience#Health#:H.npnct19.log`
## `myCategory.fctrStyles##Fashion:H.npnct19.log` ***
## `myCategory.fctrStyles#U.S.#:H.npnct19.log`
## `myCategory.fctrTravel#Travel#:H.npnct19.log`
## `myCategory.fctrTStyle##:H.npnct19.log` ***
## `myCategory.fctr##:PubDate.day.minutes.poly.1`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` ***
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` ***
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` *
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` ***
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` ***
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` ***
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` ***
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` ***
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` ***
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1`
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` *
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` ***
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` ***
## `myCategory.fctr##:PubDate.day.minutes.poly.2`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` ***
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` ***
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` ***
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` ***
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` ***
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` ***
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` ***
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` ***
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2`
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` *
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` ***
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` ***
## `myCategory.fctr##:PubDate.day.minutes.poly.3`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` ***
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` ***
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` ***
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` ***
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` ***
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` ***
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` ***
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` ***
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` *
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` .
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` ***
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` ***
## `myCategory.fctr##:PubDate.day.minutes.poly.4`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` ***
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` ***
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` *
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` ***
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` ***
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` ***
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` ***
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` ***
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` ***
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` .
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` .
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` ***
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` ***
## `myCategory.fctr##:PubDate.day.minutes.poly.5`
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` ***
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5`
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5`
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` ***
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5`
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` ***
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` ***
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` ***
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` ***
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5`
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` ***
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5`
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` ***
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5`
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5`
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` ***
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5`
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5`
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5900.1 on 6531 degrees of freedom
## Residual deviance: 16087.9 on 6306 degrees of freedom
## AIC: 16540
##
## Number of Fisher Scoring iterations: 25
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.2866885
## 2 0.1 0.6914524
## 3 0.2 0.7221172
## 4 0.3 0.7337662
## 5 0.4 0.7281184
## 6 0.5 0.7271103
## 7 0.6 0.7177799
## 8 0.7 0.6585859
## 9 0.8 0.5772627
## 10 0.9 0.3096061
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## Popular.fctr Popular.fctr.predict.Final.glm.N
## 1 N 4972
## 2 Y 189
## Popular.fctr.predict.Final.glm.Y
## 1 467
## 2 904
## Prediction
## Reference N Y
## N 4972 467
## Y 189 904
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.995713e-01 6.728475e-01 8.920264e-01 9.067574e-01 8.326699e-01
## AccuracyPValue McnemarPValue
## 6.275475e-54 2.921875e-27## Warning in mypredict_mdl(mdl, df = fit_df, rsp_var, rsp_var_out,
## model_id_method, : Expecting 1 metric: Accuracy; recd: Accuracy, Kappa;
## retaining Accuracy only
## model_id model_method
## 1 Final.glm glm
## feats
## 1 myCategory.fctr*WordCount.log, myCategory.fctr:.clusterid.fctr, myCategory.fctr*H.npnct19.log, myCategory.fctr:PubDate.day.minutes.poly.1, myCategory.fctr:PubDate.day.minutes.poly.2, myCategory.fctr:PubDate.day.minutes.poly.3, myCategory.fctr:PubDate.day.minutes.poly.4, myCategory.fctr:PubDate.day.minutes.poly.5
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 294.306 148.248
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9079351 0.3 0.7337662 0.8906892
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit min.aic.fit
## 1 0.8920264 0.9067574 0.5916591 16539.94
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01839479 0.07735006rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 14 fit.data.training 8 0 1321.857 1625.334 303.477
## 15 fit.data.training 8 1 1625.334 NA NAglb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Using default probability threshold: 0.5## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleadingsav_featsimp_df <- glb_featsimp_df
#glb_feats_df <- sav_feats_df
# glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
# entity_df=glb_trnobs_df)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl, featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## Rank9.Bayes.glm.glm.importance
## `myCategory.fctrCulture#Arts#:WordCount.log` 2.157110e+01
## `myCategory.fctrTStyle##` 8.451106e+00
## `myCategory.fctrCulture#Arts#` 1.942482e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 4.119993e-03
## `myCategory.fctrTStyle##:WordCount.log` 7.922531e-01
## `myCategory.fctrBusiness#Technology#` 6.909644e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 1.954478e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 1.271906e+01
## `myCategory.fctrBusiness#Technology#:WordCount.log` 6.393663e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 1.052033e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 3.303799e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 5.166561e-03
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 1.537242e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 9.320117e+00
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 1.641889e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 2.827267e-12
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 1.848269e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 3.267741e-13
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 1.456803e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 3.504635e-13
## `myCategory.fctr#U.S.#Education:WordCount.log` 6.684324e-04
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 4.945465e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 3.127465e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 5.485891e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 3.425300e-03
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 1.304453e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 5.777986e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 7.111804e+00
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 5.267173e+00
## `myCategory.fctrForeign#World#Asia Pacific` 1.352566e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 3.505663e-12
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 5.416985e-12
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 3.828895e-09
## `myCategory.fctrmyOther:WordCount.log` 2.523366e-04
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 1.711854e-09
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 1.653240e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 9.764945e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 4.512925e-03
## `myCategory.fctr#Multimedia#` 1.271788e-03
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 1.750250e-09
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 1.273225e-09
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 7.968954e+00
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 2.932202e-09
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 5.030792e-03
## `myCategory.fctrBusiness#Business Day#Small Business` 1.809042e+00
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 9.917204e+00
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 4.058020e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 3.575217e+00
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 2.468260e-01
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 5.039579e-11
## `myCategory.fctr#Multimedia#:WordCount.log` 3.307688e+00
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` NA
## `myCategory.fctrBusiness#Crosswords/Games#` 4.843395e-05
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 4.828695e-05
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 2.412686e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` 4.394841e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 4.852911e-05
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 4.863348e-05
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 4.861938e-05
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 1.825427e+00
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 2.504980e-01
## `myCategory.fctrStyles##Fashion:WordCount.log` 2.378297e-04
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 1.668170e+01
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 2.034420e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 2.799343e-03
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 6.114696e-04
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 1.313310e+00
## myCategory.fctrmyOther 2.288603e-04
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 4.652878e-11
## `myCategory.fctrTStyle##:H.npnct19.log` 8.624905e-04
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 4.850624e-05
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 2.034308e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 1.851233e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 7.289080e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 1.066652e+01
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` 1.504956e+01
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` 1.492789e+01
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 4.089742e-11
## `myCategory.fctrStyles##Fashion` 2.853249e-04
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 1.168596e-04
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 2.036630e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 4.129570e-11
## `myCategory.fctrmyOther:H.npnct19.log` 7.394921e-05
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 4.360351e-11
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 6.150200e-04
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 4.847880e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 1.054340e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 1.462603e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 4.248833e-04
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 2.857529e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 2.722640e-04
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 1.189402e+01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 3.201436e-04
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 2.499844e-04
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 8.643218e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 8.993313e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 4.945572e-04
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 1.803981e+01
## `myCategory.fctr#U.S.#Education` 1.888960e-04
## WordCount.log 9.884070e+01
## `myCategory.fctrOpEd#Opinion#` 1.000000e+02
## `myCategory.fctrOpEd#Opinion#:WordCount.log` 7.815739e+01
## `myCategory.fctrStyles#U.S.#` 4.000256e+01
## `myCategory.fctrStyles#U.S.#:WordCount.log` 3.120595e+01
## H.npnct19.log 5.218153e+01
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` 4.268518e+01
## `myCategory.fctr##:.clusterid.fctr2` 4.103112e+01
## `myCategory.fctrMetro#N.Y. / Region#` 4.645295e+01
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` 4.408266e+01
## `myCategory.fctrBusiness#Business Day#Dealbook` 3.380594e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 3.018292e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 2.696428e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 2.832896e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 2.054855e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 2.549705e+01
## `myCategory.fctr##:.clusterid.fctr16` 2.419378e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 2.920092e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 3.552842e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 2.363321e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 1.307802e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 2.141486e+01
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 2.049585e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 1.644645e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 1.924374e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 2.259505e+01
## `myCategory.fctr##:.clusterid.fctr12` 1.276039e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 3.138751e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 2.268658e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 1.181831e+01
## `myCategory.fctr##:.clusterid.fctr5` 2.157671e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 1.856946e+01
## `myCategory.fctrScience#Health#:WordCount.log` 9.166309e+00
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 1.458054e+01
## `myCategory.fctr##:.clusterid.fctr20` 3.147075e-03
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 1.266601e+01
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 2.321685e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 1.565868e+01
## `myCategory.fctrScience#Health#:H.npnct19.log` 1.490172e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 1.126736e+01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 6.374634e+00
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 4.858091e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 1.055727e+01
## `myCategory.fctr##:.clusterid.fctr18` 1.314696e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 1.295186e+01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 1.339390e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 7.926461e+00
## `myCategory.fctr##:.clusterid.fctr8` 2.844186e+00
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 9.865082e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 2.018289e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.130503e+00
## `myCategory.fctr##:.clusterid.fctr3` 1.540990e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 3.726561e+00
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 1.314288e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 8.744250e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 1.460209e+01
## `myCategory.fctr##:.clusterid.fctr7` 2.265546e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 6.561348e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 5.154312e+00
## `myCategory.fctr##:.clusterid.fctr6` 4.385923e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 6.167661e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 1.319672e+01
## `myCategory.fctr##:.clusterid.fctr14` 5.165276e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 6.259496e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 6.405127e-01
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 6.618637e+00
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 1.269929e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 4.917465e-03
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 1.314612e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 1.047047e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 1.862809e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 4.901225e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 5.547585e+00
## `myCategory.fctr##:.clusterid.fctr11` 6.349161e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 4.575708e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 3.689364e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 6.457157e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 1.843274e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 2.567929e+00
## `myCategory.fctr##:.clusterid.fctr17` 1.587524e+00
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 3.912793e+00
## `myCategory.fctr##:.clusterid.fctr13` 2.624995e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 2.521825e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 6.397139e+00
## `myCategory.fctr##:.clusterid.fctr19` 2.497297e+00
## `myCategory.fctr#Opinion#Room For Debate` 6.275751e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 5.321709e+00
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 3.238308e-01
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 2.871034e+00
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 6.083262e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 1.553186e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 2.260944e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 1.396836e+00
## `myCategory.fctrScience#Health#` 6.863985e+00
## `myCategory.fctr##:.clusterid.fctr15` 3.038014e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 2.116159e+00
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 1.187027e+01
## `myCategory.fctr##:.clusterid.fctr9` 1.314042e+01
## `myCategory.fctr#Opinion#The Public Editor` 1.230877e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 1.062662e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 8.954267e-06
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 5.266618e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 5.256863e-05
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` 3.940152e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 6.095186e-05
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 1.289043e-03
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 6.970142e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 6.670098e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 6.880603e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 6.800621e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 6.729538e-04
## `myCategory.fctrTravel#Travel#:WordCount.log` 7.039994e-03
## `myCategory.fctrTravel#Travel#` 1.855409e-04
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 4.645849e-03
## `myCategory.fctr##:.clusterid.fctr4` 5.667737e-03
## `myCategory.fctr##:.clusterid.fctr10` 7.291866e-03
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 3.361237e-03
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 4.462171e-04
## `myCategory.fctrForeign#World#:WordCount.log` 4.178432e-04
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 1.265256e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 0.000000e+00
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 3.271409e-13
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 1.162710e-12
## `myCategory.fctrForeign#World#:H.npnct19.log` 8.573706e-05
## `myCategory.fctrForeign#World#` 2.278260e-04
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 4.740783e-13
## importance
## `myCategory.fctrCulture#Arts#:WordCount.log` 1.000000e+02
## `myCategory.fctrTStyle##` 8.311105e+01
## `myCategory.fctrCulture#Arts#` 7.644081e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 7.564265e+01
## `myCategory.fctrTStyle##:WordCount.log` 6.388548e+01
## `myCategory.fctrBusiness#Technology#` 5.789747e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 5.081137e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 4.915037e+01
## `myCategory.fctrBusiness#Technology#:WordCount.log` 4.838009e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 4.699404e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 4.667580e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 4.261894e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 4.061727e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 4.028979e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 3.468364e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 3.411597e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 3.394719e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 3.353739e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 3.311796e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 3.234696e+01
## `myCategory.fctr#U.S.#Education:WordCount.log` 3.122810e+01
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 3.064232e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 2.957532e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 2.937831e+01
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 2.931514e+01
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 2.861770e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 2.644333e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 2.577686e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 2.381665e+01
## `myCategory.fctrForeign#World#Asia Pacific` 2.372835e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 2.246495e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 2.132648e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 2.076501e+01
## `myCategory.fctrmyOther:WordCount.log` 2.012408e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 2.002402e+01
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 1.975440e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 1.956538e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 1.946063e+01
## `myCategory.fctr#Multimedia#` 1.943357e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 1.928501e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 1.837710e+01
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 1.774488e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 1.722337e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 1.542691e+01
## `myCategory.fctrBusiness#Business Day#Small Business` 1.529963e+01
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 1.498664e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 1.385439e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 1.289826e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 1.272176e+01
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 1.203077e+01
## `myCategory.fctr#Multimedia#:WordCount.log` 1.018354e+01
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` 9.963702e+00
## `myCategory.fctrBusiness#Crosswords/Games#` 9.738763e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 9.281181e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 9.198535e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` 9.176585e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 9.072122e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 8.890255e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 8.662986e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 8.640854e+00
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 8.590796e+00
## `myCategory.fctrStyles##Fashion:WordCount.log` 8.468462e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 8.347016e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 8.071021e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 7.915168e+00
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 7.797508e+00
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 7.621937e+00
## myCategory.fctrmyOther 7.538994e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 7.532345e+00
## `myCategory.fctrTStyle##:H.npnct19.log` 7.296395e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 7.143572e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 6.831876e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 6.780277e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 6.385212e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 5.970857e+00
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` 5.608472e+00
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` 5.024865e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 4.858184e+00
## `myCategory.fctrStyles##Fashion` 4.628230e+00
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 4.546770e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 4.519862e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 4.490412e+00
## `myCategory.fctrmyOther:H.npnct19.log` 4.473873e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 4.398434e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 4.321850e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 4.268185e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 4.063579e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 3.731183e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 3.595618e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 2.721288e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 2.536587e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 2.431814e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 2.179798e+00
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 1.870565e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 1.172059e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 8.291082e-01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 6.183152e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 4.230747e-01
## `myCategory.fctr#U.S.#Education` 1.128035e-01
## WordCount.log 2.890719e-06
## `myCategory.fctrOpEd#Opinion#` 2.791977e-06
## `myCategory.fctrOpEd#Opinion#:WordCount.log` 2.076892e-06
## `myCategory.fctrStyles#U.S.#` 1.738666e-06
## `myCategory.fctrStyles#U.S.#:WordCount.log` 1.543406e-06
## H.npnct19.log 1.327560e-06
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` 1.200398e-06
## `myCategory.fctr##:.clusterid.fctr2` 1.180598e-06
## `myCategory.fctrMetro#N.Y. / Region#` 1.164007e-06
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` 1.109756e-06
## `myCategory.fctrBusiness#Business Day#Dealbook` 1.089080e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 1.009766e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 8.293746e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 7.329648e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 6.901887e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 6.819898e-07
## `myCategory.fctr##:.clusterid.fctr16` 6.754565e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 6.570987e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 6.541200e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 6.252462e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 6.165807e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 6.135461e-07
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 6.079845e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 6.024787e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 5.326887e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 5.274079e-07
## `myCategory.fctr##:.clusterid.fctr12` 5.086707e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 5.014849e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 4.900493e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 4.851447e-07
## `myCategory.fctr##:.clusterid.fctr5` 4.762285e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 4.657528e-07
## `myCategory.fctrScience#Health#:WordCount.log` 4.603708e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 4.555825e-07
## `myCategory.fctr##:.clusterid.fctr20` 4.275494e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 4.271930e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 4.264207e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 4.211972e-07
## `myCategory.fctrScience#Health#:H.npnct19.log` 3.954463e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 3.914690e-07
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 3.869019e-07
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 3.698206e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 3.696088e-07
## `myCategory.fctr##:.clusterid.fctr18` 3.577092e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 3.548080e-07
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 3.355832e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 3.330807e-07
## `myCategory.fctr##:.clusterid.fctr8` 3.269255e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 3.233295e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 2.996324e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 2.964284e-07
## `myCategory.fctr##:.clusterid.fctr3` 2.917214e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 2.808542e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 2.772469e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 2.573693e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 2.548318e-07
## `myCategory.fctr##:.clusterid.fctr7` 2.472192e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 2.406889e-07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 2.385979e-07
## `myCategory.fctr##:.clusterid.fctr6` 2.373713e-07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 2.317193e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 2.246618e-07
## `myCategory.fctr##:.clusterid.fctr14` 2.209964e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 2.168190e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 2.004393e-07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 1.950588e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 1.867048e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 1.752121e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 1.677753e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 1.657333e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 1.529064e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 1.505545e-07
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 1.359223e-07
## `myCategory.fctr##:.clusterid.fctr11` 1.320384e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 1.312657e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 1.295936e-07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 1.261809e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 1.193881e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.149712e-07
## `myCategory.fctr##:.clusterid.fctr17` 1.118334e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 1.078283e-07
## `myCategory.fctr##:.clusterid.fctr13` 1.000078e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 9.884531e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 9.804680e-08
## `myCategory.fctr##:.clusterid.fctr19` 8.479922e-08
## `myCategory.fctr#Opinion#Room For Debate` 8.269772e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 8.262827e-08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 7.302999e-08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 6.750718e-08
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 6.076050e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 5.686369e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 5.063688e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 4.481444e-08
## `myCategory.fctrScience#Health#` 3.455968e-08
## `myCategory.fctr##:.clusterid.fctr15` 2.330854e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 1.992107e-08
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 1.601935e-08
## `myCategory.fctr##:.clusterid.fctr9` 7.494891e-09
## `myCategory.fctr#Opinion#The Public Editor` 5.139739e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 5.113902e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 5.104444e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 5.059346e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 5.041912e-09
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` 5.012660e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 4.985583e-09
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 4.299126e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 5.752182e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 5.591499e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 5.529316e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 5.457561e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 5.408422e-10
## `myCategory.fctrTravel#Travel#:WordCount.log` 4.503984e-10
## `myCategory.fctrTravel#Travel#` 3.286422e-10
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 2.096444e-10
## `myCategory.fctr##:.clusterid.fctr4` 1.917750e-10
## `myCategory.fctr##:.clusterid.fctr10` 1.646286e-10
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 8.905714e-11
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 3.329893e-11
## `myCategory.fctrForeign#World#:WordCount.log` 1.953007e-11
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 6.711045e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 3.694757e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 3.071714e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 2.380649e-12
## `myCategory.fctrForeign#World#:H.npnct19.log` 1.590428e-12
## `myCategory.fctrForeign#World#` 1.041456e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 0.000000e+00
## Final.glm.importance
## `myCategory.fctrCulture#Arts#:WordCount.log` 1.000000e+02
## `myCategory.fctrTStyle##` 8.311105e+01
## `myCategory.fctrCulture#Arts#` 7.644081e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 7.564265e+01
## `myCategory.fctrTStyle##:WordCount.log` 6.388548e+01
## `myCategory.fctrBusiness#Technology#` 5.789747e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 5.081137e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 4.915037e+01
## `myCategory.fctrBusiness#Technology#:WordCount.log` 4.838009e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 4.699404e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 4.667580e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 4.261894e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 4.061727e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 4.028979e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 3.468364e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 3.411597e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 3.394719e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 3.353739e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 3.311796e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 3.234696e+01
## `myCategory.fctr#U.S.#Education:WordCount.log` 3.122810e+01
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 3.064232e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 2.957532e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 2.937831e+01
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 2.931514e+01
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 2.861770e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 2.644333e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 2.577686e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 2.381665e+01
## `myCategory.fctrForeign#World#Asia Pacific` 2.372835e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 2.246495e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 2.132648e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 2.076501e+01
## `myCategory.fctrmyOther:WordCount.log` 2.012408e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 2.002402e+01
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 1.975440e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 1.956538e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 1.946063e+01
## `myCategory.fctr#Multimedia#` 1.943357e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 1.928501e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 1.837710e+01
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 1.774488e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 1.722337e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 1.542691e+01
## `myCategory.fctrBusiness#Business Day#Small Business` 1.529963e+01
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 1.498664e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 1.385439e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 1.289826e+01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 1.272176e+01
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 1.203077e+01
## `myCategory.fctr#Multimedia#:WordCount.log` 1.018354e+01
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` 9.963702e+00
## `myCategory.fctrBusiness#Crosswords/Games#` 9.738763e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 9.281181e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 9.198535e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` 9.176585e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 9.072122e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 8.890255e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 8.662986e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 8.640854e+00
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 8.590796e+00
## `myCategory.fctrStyles##Fashion:WordCount.log` 8.468462e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 8.347016e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 8.071021e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 7.915168e+00
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 7.797508e+00
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 7.621937e+00
## myCategory.fctrmyOther 7.538994e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 7.532345e+00
## `myCategory.fctrTStyle##:H.npnct19.log` 7.296395e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 7.143572e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 6.831876e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 6.780277e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 6.385212e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 5.970857e+00
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` 5.608472e+00
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` 5.024865e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 4.858184e+00
## `myCategory.fctrStyles##Fashion` 4.628230e+00
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 4.546770e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 4.519862e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 4.490412e+00
## `myCategory.fctrmyOther:H.npnct19.log` 4.473873e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 4.398434e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 4.321850e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 4.268185e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 4.063579e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 3.731183e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 3.595618e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 2.721288e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 2.536587e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 2.431814e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 2.179798e+00
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 1.870565e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 1.172059e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 8.291082e-01
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 6.183152e-01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 4.230747e-01
## `myCategory.fctr#U.S.#Education` 1.128035e-01
## WordCount.log 2.890719e-06
## `myCategory.fctrOpEd#Opinion#` 2.791977e-06
## `myCategory.fctrOpEd#Opinion#:WordCount.log` 2.076892e-06
## `myCategory.fctrStyles#U.S.#` 1.738666e-06
## `myCategory.fctrStyles#U.S.#:WordCount.log` 1.543406e-06
## H.npnct19.log 1.327560e-06
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` 1.200398e-06
## `myCategory.fctr##:.clusterid.fctr2` 1.180598e-06
## `myCategory.fctrMetro#N.Y. / Region#` 1.164007e-06
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` 1.109756e-06
## `myCategory.fctrBusiness#Business Day#Dealbook` 1.089080e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 1.009766e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 8.293746e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 7.329648e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 6.901887e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 6.819898e-07
## `myCategory.fctr##:.clusterid.fctr16` 6.754565e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 6.570987e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 6.541200e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 6.252462e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 6.165807e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 6.135461e-07
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 6.079845e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 6.024787e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 5.326887e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 5.274079e-07
## `myCategory.fctr##:.clusterid.fctr12` 5.086707e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 5.014849e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 4.900493e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 4.851447e-07
## `myCategory.fctr##:.clusterid.fctr5` 4.762285e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 4.657528e-07
## `myCategory.fctrScience#Health#:WordCount.log` 4.603708e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 4.555825e-07
## `myCategory.fctr##:.clusterid.fctr20` 4.275494e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 4.271930e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 4.264207e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 4.211972e-07
## `myCategory.fctrScience#Health#:H.npnct19.log` 3.954463e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 3.914690e-07
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 3.869019e-07
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 3.698206e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 3.696088e-07
## `myCategory.fctr##:.clusterid.fctr18` 3.577092e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 3.548080e-07
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 3.355832e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 3.330807e-07
## `myCategory.fctr##:.clusterid.fctr8` 3.269255e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 3.233295e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 2.996324e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 2.964284e-07
## `myCategory.fctr##:.clusterid.fctr3` 2.917214e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 2.808542e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 2.772469e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 2.573693e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 2.548318e-07
## `myCategory.fctr##:.clusterid.fctr7` 2.472192e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 2.406889e-07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 2.385979e-07
## `myCategory.fctr##:.clusterid.fctr6` 2.373713e-07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 2.317193e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 2.246618e-07
## `myCategory.fctr##:.clusterid.fctr14` 2.209964e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 2.168190e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 2.004393e-07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 1.950588e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 1.867048e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 1.752121e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 1.677753e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 1.657333e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 1.529064e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 1.505545e-07
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 1.359223e-07
## `myCategory.fctr##:.clusterid.fctr11` 1.320384e-07
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 1.312657e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 1.295936e-07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 1.261809e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 1.193881e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.149712e-07
## `myCategory.fctr##:.clusterid.fctr17` 1.118334e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 1.078283e-07
## `myCategory.fctr##:.clusterid.fctr13` 1.000078e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 9.884531e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 9.804680e-08
## `myCategory.fctr##:.clusterid.fctr19` 8.479922e-08
## `myCategory.fctr#Opinion#Room For Debate` 8.269772e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 8.262827e-08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 7.302999e-08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 6.750718e-08
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 6.076050e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 5.686369e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 5.063688e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 4.481444e-08
## `myCategory.fctrScience#Health#` 3.455968e-08
## `myCategory.fctr##:.clusterid.fctr15` 2.330854e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 1.992107e-08
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 1.601935e-08
## `myCategory.fctr##:.clusterid.fctr9` 7.494891e-09
## `myCategory.fctr#Opinion#The Public Editor` 5.139739e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 5.113902e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 5.104444e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 5.059346e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 5.041912e-09
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` 5.012660e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 4.985583e-09
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 4.299126e-09
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 5.752182e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 5.591499e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 5.529316e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 5.457561e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 5.408422e-10
## `myCategory.fctrTravel#Travel#:WordCount.log` 4.503984e-10
## `myCategory.fctrTravel#Travel#` 3.286422e-10
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 2.096444e-10
## `myCategory.fctr##:.clusterid.fctr4` 1.917750e-10
## `myCategory.fctr##:.clusterid.fctr10` 1.646286e-10
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 8.905714e-11
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 3.329893e-11
## `myCategory.fctrForeign#World#:WordCount.log` 1.953007e-11
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 6.711045e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 3.694757e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 3.071714e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 2.380649e-12
## `myCategory.fctrForeign#World#:H.npnct19.log` 1.590428e-12
## `myCategory.fctrForeign#World#` 1.041456e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 0.000000e+00glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 11
## [1] "Min/Max Boundaries: "
## [1] UniqueID
## [2] Popular.fctr
## [3] Popular.fctr.predict.Final.glm.prob
## [4] Popular.fctr.predict.Final.glm
## [5] Popular.fctr.predict.Final.glm.accurate
## [6] Popular.fctr.predict.Final.glm.error
## [7] .label
## <0 rows> (or 0-length row.names)
## [1] "Inaccurate: "
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## 172 172 Y 2.220446e-16
## 234 234 Y 2.220446e-16
## 391 391 Y 2.220446e-16
## 360 360 Y 2.220446e-16
## 438 438 Y 2.220446e-16
## 420 420 Y 2.220446e-16
## Popular.fctr.predict.Final.glm Popular.fctr.predict.Final.glm.accurate
## 172 N FALSE
## 234 N FALSE
## 391 N FALSE
## 360 N FALSE
## 438 N FALSE
## 420 N FALSE
## Popular.fctr.predict.Final.glm.error
## 172 -0.5
## 234 -0.5
## 391 -0.5
## 360 -0.5
## 438 -0.5
## 420 -0.5
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## 3193 3193 Y 2.220446e-16
## 4673 4673 N 5.132549e-01
## 354 354 N 6.812200e-01
## 879 879 N 1.000000e+00
## 3895 3895 N 1.000000e+00
## 6017 6017 N 1.000000e+00
## Popular.fctr.predict.Final.glm
## 3193 N
## 4673 Y
## 354 Y
## 879 Y
## 3895 Y
## 6017 Y
## Popular.fctr.predict.Final.glm.accurate
## 3193 FALSE
## 4673 FALSE
## 354 FALSE
## 879 FALSE
## 3895 FALSE
## 6017 FALSE
## Popular.fctr.predict.Final.glm.error
## 3193 -0.50000000
## 4673 0.01325493
## 354 0.18122004
## 879 0.50000000
## 3895 0.50000000
## 6017 0.50000000
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## 6276 6276 N 1
## 6229 6229 N 1
## 6440 6440 N 1
## 6474 6474 N 1
## 6506 6506 N 1
## 6532 6532 N 1
## Popular.fctr.predict.Final.glm
## 6276 Y
## 6229 Y
## 6440 Y
## 6474 Y
## 6506 Y
## 6532 Y
## Popular.fctr.predict.Final.glm.accurate
## 6276 FALSE
## 6229 FALSE
## 6440 FALSE
## 6474 FALSE
## 6506 FALSE
## 6532 FALSE
## Popular.fctr.predict.Final.glm.error
## 6276 0.5
## 6229 0.5
## 6440 0.5
## 6474 0.5
## 6506 0.5
## 6532 0.5
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])## Popular.fctr Popular.fctr.predict.Final.glm.prob
## 92 Y 3.440292e-03
## 693 Y 2.220446e-16
## 4020 Y 3.330309e-02
## 4721 Y 2.220446e-16
## Popular.fctr.predict.Final.glm
## 92 N
## 693 N
## 4020 N
## 4721 Nprint(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "Popular.fctr.predict.Final.glm.prob"
## [2] "Popular.fctr.predict.Final.glm"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 15 fit.data.training 8 1 1625.334 1638.037 12.703
## 16 predict.data.new 9 0 1638.038 NA NAStep 9.0: predict data new
# Compute final model predictions
glb_newobs_df <- glb_get_predictions(glb_newobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(glb_newobs_df, mdl_id = glb_fin_mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.5## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleadingglb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 11## Warning in loop_apply(n, do.ply): no non-missing arguments to min;
## returning Inf## Warning in loop_apply(n, do.ply): no non-missing arguments to max;
## returning -Inf## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).
## Warning in loop_apply(n, do.ply): no non-missing arguments to min;
## returning Inf## Warning in loop_apply(n, do.ply): no non-missing arguments to max;
## returning -Inf## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).
## Warning in loop_apply(n, do.ply): no non-missing arguments to min;
## returning Inf## Warning in loop_apply(n, do.ply): no non-missing arguments to max;
## returning -Inf## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).
## Warning in loop_apply(n, do.ply): no non-missing arguments to min;
## returning Inf## Warning in loop_apply(n, do.ply): no non-missing arguments to max;
## returning -Inf## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).
## Warning in loop_apply(n, do.ply): no non-missing arguments to min;
## returning Inf## Warning in loop_apply(n, do.ply): no non-missing arguments to max;
## returning -Inf## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).
## [1] "Min/Max Boundaries: "
## [1] UniqueID
## [2] Popular.fctr
## [3] Popular.fctr.predict.Final.glm.prob
## [4] Popular.fctr.predict.Final.glm
## [5] Popular.fctr.predict.Final.glm.accurate
## [6] Popular.fctr.predict.Final.glm.error
## [7] .label
## <0 rows> (or 0-length row.names)
## [1] "Inaccurate: "
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## NA NA <NA> NA
## NA.1 NA <NA> NA
## NA.2 NA <NA> NA
## NA.3 NA <NA> NA
## NA.4 NA <NA> NA
## NA.5 NA <NA> NA
## Popular.fctr.predict.Final.glm
## NA <NA>
## NA.1 <NA>
## NA.2 <NA>
## NA.3 <NA>
## NA.4 <NA>
## NA.5 <NA>
## Popular.fctr.predict.Final.glm.accurate
## NA NA
## NA.1 NA
## NA.2 NA
## NA.3 NA
## NA.4 NA
## NA.5 NA
## Popular.fctr.predict.Final.glm.error
## NA NA
## NA.1 NA
## NA.2 NA
## NA.3 NA
## NA.4 NA
## NA.5 NA
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## NA.168 NA <NA> NA
## NA.243 NA <NA> NA
## NA.261 NA <NA> NA
## NA.1464 NA <NA> NA
## NA.1493 NA <NA> NA
## NA.1501 NA <NA> NA
## Popular.fctr.predict.Final.glm
## NA.168 <NA>
## NA.243 <NA>
## NA.261 <NA>
## NA.1464 <NA>
## NA.1493 <NA>
## NA.1501 <NA>
## Popular.fctr.predict.Final.glm.accurate
## NA.168 NA
## NA.243 NA
## NA.261 NA
## NA.1464 NA
## NA.1493 NA
## NA.1501 NA
## Popular.fctr.predict.Final.glm.error
## NA.168 NA
## NA.243 NA
## NA.261 NA
## NA.1464 NA
## NA.1493 NA
## NA.1501 NA
## UniqueID Popular.fctr Popular.fctr.predict.Final.glm.prob
## NA.1864 NA <NA> NA
## NA.1865 NA <NA> NA
## NA.1866 NA <NA> NA
## NA.1867 NA <NA> NA
## NA.1868 NA <NA> NA
## NA.1869 NA <NA> NA
## Popular.fctr.predict.Final.glm
## NA.1864 <NA>
## NA.1865 <NA>
## NA.1866 <NA>
## NA.1867 <NA>
## NA.1868 <NA>
## NA.1869 <NA>
## Popular.fctr.predict.Final.glm.accurate
## NA.1864 NA
## NA.1865 NA
## NA.1866 NA
## NA.1867 NA
## NA.1868 NA
## NA.1869 NA
## Popular.fctr.predict.Final.glm.error
## NA.1864 NA
## NA.1865 NA
## NA.1866 NA
## NA.1867 NA
## NA.1868 NA
## NA.1869 NA## Warning in loop_apply(n, do.ply): Removed 1870 rows containing missing
## values (geom_point).
submit_df <- glb_newobs_df[, c(glb_id_vars,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
write.csv(submit_df,
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_fin_mdl_id), fixed=TRUE),
"_submit.csv"), row.names=FALSE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("model_id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
print(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])## [1] 0.5print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: Rank9.Bayes.glm.glm"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.glm"print(dim(glb_fitobs_df))## [1] 4475 281print(dsp_models_df)## model_id max.Accuracy.OOB
## Rank9.Bayes.glm.glm Rank9.Bayes.glm.glm 0.9149246
## Low.cor.X.glm Low.cor.X.glm 0.9076325
## All.X.2.glm All.X.2.glm 0.9066602
## All.X.glm All.X.glm 0.9022849
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart 0.8862421
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm 0.8342246
## MFO.myMFO_classfr MFO.myMFO_classfr 0.8327662
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart 0.8327662
## Max.cor.Y.rpart Max.cor.Y.rpart 0.8327662
## Max.cor.Y.TmSrs.poly.glm Max.cor.Y.TmSrs.poly.glm 0.7661643
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart 0.7345649
## Max.cor.Y.glm Max.cor.Y.glm 0.7292173
## Random.myrandom_classfr Random.myrandom_classfr 0.1672338
## max.auc.OOB max.Kappa.OOB min.aic.fit
## Rank9.Bayes.glm.glm 0.9326203 0.6934914 2129.829
## Low.cor.X.glm 0.9169373 0.6676067 2101.413
## All.X.2.glm 0.9104268 0.6664381 2243.694
## All.X.glm 0.9084175 0.6536059 2161.079
## All.X.no.rnorm.rpart 0.7084504 0.5054039 NA
## Interact.High.cor.Y.glm 0.8577584 0.4560113 2857.963
## MFO.myMFO_classfr 0.5000000 0.0000000 NA
## Max.cor.Y.cv.0.rpart 0.5000000 0.0000000 NA
## Max.cor.Y.rpart 0.5000000 0.0000000 NA
## Max.cor.Y.TmSrs.poly.glm 0.8083559 0.3637267 3255.592
## Max.cor.Y.cv.0.cp.0.rpart 0.7482444 0.2680784 NA
## Max.cor.Y.glm 0.7836398 0.2846066 3398.567
## Random.myrandom_classfr 0.4877001 0.0000000 NA
## opt.prob.threshold.OOB
## Rank9.Bayes.glm.glm 0.5
## Low.cor.X.glm 0.4
## All.X.2.glm 0.4
## All.X.glm 0.4
## All.X.no.rnorm.rpart 0.7
## Interact.High.cor.Y.glm 0.3
## MFO.myMFO_classfr 0.5
## Max.cor.Y.cv.0.rpart 0.5
## Max.cor.Y.rpart 0.5
## Max.cor.Y.TmSrs.poly.glm 0.2
## Max.cor.Y.cv.0.cp.0.rpart 0.2
## Max.cor.Y.glm 0.2
## Random.myrandom_classfr 0.1print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))## [1] "Rank9.Bayes.glm.glm OOB confusion matrix & accuracy: "print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))## Prediction
## Reference N Y
## N 1627 86
## Y 89 255tmp_OOBent_df <- glb_OOBobs_df[, c("myCategory", predct_accurate_var_name)]
names(tmp_OOBent_df)[2] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBent_df, names(tmp_OOBent_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))## [1] "myCategory" ".n.OOB"glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))## myCategory .n.OOB .n.Tst .freqRatio.Tst
## 1 ## 407 338 0.180748663
## 15 OpEd#Opinion# 154 164 0.087700535
## 6 Business#Business Day#Dealbook 312 304 0.162566845
## 18 Styles#U.S.# 54 62 0.033155080
## 10 Culture#Arts# 225 244 0.130481283
## 9 Business#Technology# 114 113 0.060427807
## 16 Science#Health# 66 57 0.030481283
## 8 Business#Crosswords/Games# 40 42 0.022459893
## 3 #Opinion#Room For Debate 21 24 0.012834225
## 4 #Opinion#The Public Editor 10 10 0.005347594
## 7 Business#Business Day#Small Business 45 42 0.022459893
## 13 Metro#N.Y. / Region# 60 67 0.035828877
## 20 TStyle## 221 105 0.056149733
## 12 Foreign#World#Asia Pacific 61 56 0.029946524
## 17 Styles##Fashion 41 15 0.008021390
## 19 Travel#Travel# 31 35 0.018716578
## 2 #Multimedia# 42 52 0.027807487
## 5 #U.S.#Education 93 90 0.048128342
## 11 Foreign#World# 47 47 0.025133690
## 14 myOther 13 3 0.001604278
## .freqRatio.OOB accurate.OOB.FALSE accurate.OOB.TRUE max.accuracy.OOB
## 1 0.197860963 35 372 0.9140049
## 15 0.074866310 33 121 0.7857143
## 6 0.151677200 32 280 0.8974359
## 18 0.026251823 21 33 0.6111111
## 10 0.109382596 15 210 0.9333333
## 9 0.055420515 12 102 0.8947368
## 16 0.032085561 8 58 0.8787879
## 8 0.019445795 4 36 0.9000000
## 3 0.010209042 3 18 0.8571429
## 4 0.004861449 3 7 0.7000000
## 7 0.021876519 2 43 0.9555556
## 13 0.029168692 2 58 0.9666667
## 20 0.107438017 2 219 0.9909502
## 12 0.029654837 1 60 0.9836066
## 17 0.019931940 1 40 0.9756098
## 19 0.015070491 1 30 0.9677419
## 2 0.020418085 0 42 1.0000000
## 5 0.045211473 0 93 1.0000000
## 11 0.022848809 0 47 1.0000000
## 14 0.006319883 0 13 1.0000000dsp_myCategory_conf_mtrx <- function(myCategory) {
print(sprintf("%s OOB::myCategory=%s confusion matrix & accuracy: ",
glb_sel_mdl_id, myCategory))
print(t(confusionMatrix(
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, glb_rsp_var])$table))
print(sum(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
predct_accurate_var_name]) /
nrow(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, ]))
err_ids <- glb_OOBobs_df[(glb_OOBobs_df$myCategory == myCategory) &
(!glb_OOBobs_df[, predct_accurate_var_name]), glb_id_vars]
OOB_FNerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 1),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FN errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FNerr_df)))
print(OOB_FNerr_df)
OOB_FPerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 0),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FP errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FPerr_df)))
print(OOB_FPerr_df)
}
#dsp_myCategory_conf_mtrx(myCategory="OpEd#Opinion#")
#dsp_myCategory_conf_mtrx(myCategory="Business#Business Day#Dealbook")
dsp_myCategory_conf_mtrx(myCategory="##")## [1] "Rank9.Bayes.glm.glm OOB::myCategory=## confusion matrix & accuracy: "
## Prediction
## Reference N Y
## N 358 14
## Y 21 14
## [1] 0.9140049
## [1] "Rank9.Bayes.glm.glm OOB::myCategory=## FN errors: 21"
## .clusterid Popular
## 95 3 1
## 163 9 1
## 685 15 1
## 1132 9 1
## 1092 9 1
## 1404 1 1
## 1489 15 1
## 2007 11 1
## 2088 1 1
## 2123 11 1
## 2330 1 1
## 3074 1 1
## 3263 15 1
## 3472 3 1
## 3492 17 1
## 3882 5 1
## 4415 11 1
## 4632 5 1
## 4775 9 1
## 5058 20 1
## 6357 3 1
## Headline
## 95 Phrases We Love Too Much
## 163 Why Leaked Nude Photos Are Another Frontier for Feminists
## 685 What Janay Rice Wants
## 1132 Wasted Words
## 1092 How to Fake Your Next Vacation
## 1404 When Tips Are Not Enough
## 1489 What to Expect From Narendra Modi at the United Nations
## 2007 Can a Social Network Stay Ad-Free?
## 2088 Narendra Modi, in U.N. Speech, Inserts India Into Terrorism Fight
## 2123 Dont Do the Things You Love
## 2330 Fighting Human Nature
## 3074 Inside the Bounds of a Hasidic Neighborhood
## 3263 Discussion of Pedophilia Turns Heated
## 3472 Yes Means Yes: The Big Consent Debate
## 3492 Life and Death Through the Eyes of an Ebola Nurse
## 3882 To Die at Home: Reporters Notebook
## 4415 Phrases We Love Too Much
## 4632 Tim Cook's Coming Out: Reporter's Notebook
## 4775 A Midterm Election Quiz: Testing Our Political Assumptions
## 5058 Sexual Harassment at Yale: Delicate Subject, High-Impact Investigation
## 6357 What Big Thing Would Reinvigorate the Democratic Party?
## Snippet
## 95 A careful reader alerts us to a clich in the making: on the planet.
## 163 After nude photos of famous women were leaked on the Internet, many observers are asking if online harassment of celebrities and women can be reined in or punished.
## 685 What impact does the video of Ray Rice punching his wife, Janay, have on her, and should news outlets take that into consideration?
## 1132 In many cases, extraneous words gum up our prose. Heres one common construction that can almost always be improved by being shortened: The [noun] is an [adjective] one.
## 1092 A Dutch artist faked a trip to Southeast Asia through a series of carefully manipulated Facebook photos, demonstrating the power and ubiquity of deception in the digital age.
## 1404 A recent campaign aims to convince hotel guests to tip more, but critics say tips are a poor substitute for wages.
## 1489 Since taking power in May, Prime Minister Narendra Modi of India has had runaway success with major speeches in part because of their character colloquial, earthy and extemporaneous. Will that tone translate to the United Nations General...
## 2007 Ello bills itself as proudly devoid of ads but at least one brand is already advertising its product there.
## 2088 At the United Nations General Assembly, Prime Minister Narendra Modi of India signaled his support for the United States renewed focus on fighting terrorism.
## 2123 At least, says one psychologist, dont do them too often.
## 2330 Some people claim that online misogyny is just natural male behavior. But for critics, this ignores the human capacity for change.
## 3074 The Hasidic community is known for its traditions and insularity. The artist and journalist Annie Berman set out to break down the groups boundaries through an ad she places on Craigslist.
## 3263 An Op-Ed essay about what the author said were misconceptions about pedophilia drew more than 1,200 comments mostly negative, but with a wide range of views expressed.
## 3472 A longstanding debate about what constitutes consent in sexual interaction has been reignited by the conversation about sexual violence on campus and elsewhere.
## 3492 Ben C. Solomon is a Times video journalist reporting on Ebola. His video today, about a team of ambulance drivers in Monrovia, Liberia, shows the dangers they face every day.
## 3882 A journey taken with a woman and her dying father led Nina Bernstein to write an article that speaks to the challenges many readers also face.
## 4415 I try to avoid simply harping on my own pet peeves when citing phrases as overused or tired. But I cant deny that I find this expression particularly irksome: channeling ones inner something-or-other.
## 4632 James B. Stewart provides insight into his column about the Apple chief executives announcement that he is proud to be gay.
## 4775 Do you know whos a Republican or a liberal?
## 5058 Tamar Lewin talks about reporting on a sexual harassment case and the months it took to find people who would speak on the record and provide documents.
## 6357 After the midterm debacle, liberals are looking for a compelling national vision.
## Abstract
## 95 A careful reader alerts us to a clich in the making: on the planet.
## 163 After nude photos of famous women were leaked on the Internet, many observers are asking if online harassment of celebrities and women can be reined in or punished.
## 685 What impact does the video of Ray Rice punching his wife, Janay, have on her, and should news outlets take that into consideration?
## 1132 In many cases, extraneous words gum up our prose. Heres one common construction that can almost always be improved by being shortened: The [noun] is an [adjective] one.
## 1092 A Dutch artist faked a trip to Southeast Asia through a series of carefully manipulated Facebook photos, demonstrating the power and ubiquity of deception in the digital age.
## 1404 A recent campaign aims to convince hotel guests to tip more, but critics say tips are a poor substitute for wages.
## 1489 Since taking power in May, Prime Minister Narendra Modi of India has had runaway success with major speeches in part because of their character colloquial, earthy and extemporaneous. Will that tone translate to the United Nations General Assembly?
## 2007 Ello bills itself as proudly devoid of ads but at least one brand is already advertising its product there.
## 2088 At the United Nations General Assembly, Prime Minister Narendra Modi of India signaled his support for the United States renewed focus on fighting terrorism.
## 2123 At least, says one psychologist, dont do them too often.
## 2330 Some people claim that online misogyny is just natural male behavior. But for critics, this ignores the human capacity for change.
## 3074 The Hasidic community is known for its traditions and insularity. The artist and journalist Annie Berman set out to break down the groups boundaries through an ad she places on Craigslist.
## 3263 An Op-Ed essay about what the author said were misconceptions about pedophilia drew more than 1,200 comments mostly negative, but with a wide range of views expressed.
## 3472 A longstanding debate about what constitutes consent in sexual interaction has been reignited by the conversation about sexual violence on campus and elsewhere.
## 3492 Ben C. Solomon is a Times video journalist reporting on Ebola. His video today, about a team of ambulance drivers in Monrovia, Liberia, shows the dangers they face every day.
## 3882 A journey taken with a woman and her dying father led Nina Bernstein to write an article that speaks to the challenges many readers also face.
## 4415 I try to avoid simply harping on my own pet peeves when citing phrases as overused or tired. But I cant deny that I find this expression particularly irksome: channeling ones inner something-or-other.
## 4632 James B. Stewart provides insight into his column about the Apple chief executives announcement that he is proud to be gay.
## 4775 Do you know whos a Republican or a liberal?
## 5058 Tamar Lewin talks about reporting on a sexual harassment case and the months it took to find people who would speak on the record and provide documents.
## 6357 After the midterm debacle, liberals are looking for a compelling national vision.
## [1] "Rank9.Bayes.glm.glm OOB::myCategory=## FP errors: 14"
## .clusterid Popular
## 263 8 0
## 359 11 0
## 1322 12 0
## 1355 1 0
## 1714 15 0
## 1685 8 0
## 2122 15 0
## 2371 7 0
## 2483 8 0
## 3670 1 0
## 3783 1 0
## 4008 15 0
## 5625 8 0
## 5834 1 0
## Headline
## 263 Will the New Autocorrect Steal Your Soul?
## 359 Watching the Sun Set on the Summer Movie Season
## 1322 Updates on the Scottish Independence Referendum
## 1355 Do You Need a Worse Phone?
## 1714 Does Online Education Widen Offline Gaps?
## 1685 Who Needs a Cuddle Buddy? Everyone, It Turns Out
## 2122 Want a Robot Pen Pal?
## 2371 Is Gucci Grace the Next President of Zimbabwe?
## 2483 Is Hong Kongs Umbrella Revolution a New Tiananmen?
## 3670 Will an App Change Your Writing Life?
## 3783 Is American Horror Story the Best (Weirdest) Show on Television?
## 4008 What Are You Afraid Of?
## 5625 Do You Want To Know About Your Brain?
## 5834 When Is a Chocolate Shortage a Good Thing?
## Snippet
## 263 A more sophisticated form of autocorrect is coming, and one philosopher worries it will harm the way we communicate.
## 359 The Op-Ed columnists debate the union of church and real estate in Love Is Strange and explore future dystopias in The Giver. And one columnist pleads: Stop giving actors extra credit for playing gay.
## 1322 The Times is providing updates on the counting of the ballots, which stretched into the early hours of Friday morning.
## 1355 The Apple Watch is coming, but some say less-sophisticated phones are the more sophisticated choice.
## 1714 Many people praise online education as an equalizing force, but some argue that online resources can amplify certain inequities even as they lessen others.
## 1685 A new app called Cuddlr helps users locate someone to cuddle with. The idea of cuddling with a total stranger may seem uncomfortable, but it may be more important than we think.
## 2122 Robots can now write us letters. Will we read them?
## 2371 The African nation is abuzz with rumors that the 90-year-old president, Robert Mugabe, is grooming his wife to succeed him.
## 2483 Many observers compare todays protests in Hong Kong to the events of 1989 in Tiananmen Square. Will the authorities crack down as they did in 1989?
## 3670 Apps like Lists for Writers aim to help with creativity, but some question whether thats really possible.
## 3783 The FX series has revived the anthology genre, and with subject matter that both fascinates and repulses. But is it a case of genuine creative genius, or running on the fumes of hype?
## 4008 A new survey reveals that Americans fears arent necessarily rational, and it could tell us something about what fear can and cant do.
## 5625 New research suggests many people dont think that much about brain science. Should they?
## 5834 There may be less chocolate to go around and more people going for it, but there is a silver lining.
## Abstract
## 263 A more sophisticated form of autocorrect is coming, and one philosopher worries it will harm the way we communicate.
## 359 The Op-Ed columnists debate the union of church and real estate in Love Is Strange and explore future dystopias in The Giver. And one columnist pleads: Stop giving actors extra credit for playing gay.
## 1322 The Times is providing updates on the counting of the ballots, which stretched into the early hours of Friday morning.
## 1355 The Apple Watch is coming, but some say less-sophisticated phones are the more sophisticated choice.
## 1714 Many people praise online education as an equalizing force, but some argue that online resources can amplify certain inequities even as they lessen others.
## 1685 A new app called Cuddlr helps users locate someone to cuddle with. The idea of cuddling with a total stranger may seem uncomfortable, but it may be more important than we think.
## 2122 Robots can now write us letters. Will we read them?
## 2371 The African nation is abuzz with rumors that the 90-year-old president, Robert Mugabe, is grooming his wife to succeed him.
## 2483 Many observers compare todays protests in Hong Kong to the events of 1989 in Tiananmen Square. Will the authorities crack down as they did in 1989?
## 3670 Apps like Lists for Writers aim to help with creativity, but some question whether thats really possible.
## 3783 The FX series has revived the anthology genre, and with subject matter that both fascinates and repulses. But is it a case of genuine creative genius, or running on the fumes of hype?
## 4008 A new survey reveals that Americans fears arent necessarily rational, and it could tell us something about what fear can and cant do.
## 5625 New research suggests many people dont think that much about brain science. Should they?
## 5834 There may be less chocolate to go around and more people going for it, but there is a silver lining.print("FN_OOB_ids:")## [1] "FN_OOB_ids:"print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])## [1] Popular.fctr
## [2] Popular.fctr.predict.Rank9.Bayes.glm.glm.prob
## [3] Popular.fctr.predict.Rank9.Bayes.glm.glm
## [4] Popular.fctr.predict.Rank9.Bayes.glm.glm.accurate
## <0 rows> (or 0-length row.names)print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
glb_txt_vars])## [1] Headline Snippet Abstract
## <0 rows> (or 0-length row.names)print(dsp_vctr <- colSums(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
setdiff(grep("[HSA].", names(glb_OOBobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_OOBobs_df),
grep(".fctr", names(glb_OOBobs_df), fixed=TRUE, value=TRUE)))]))## PubDate.POSIX H.T.X2014
## 0 0
## H.T.X2015 H.T.art
## 0 0
## H.T.bank H.T.big
## 0 0
## H.T.billion H.T.busi
## 0 0
## H.T.china H.T.daili
## 0 0
## H.T.day H.T.deal
## 0 0
## H.T.fashion H.T.first
## 0 0
## H.T.make H.T.morn
## 0 0
## H.T.new H.T.news
## 0 0
## H.T.newyork H.T.obama
## 0 0
## H.T.pictur H.T.polit
## 0 0
## H.T.report H.T.say
## 0 0
## H.T.springsumm H.T.take
## 0 0
## H.T.test H.T.today
## 0 0
## H.T.week S.T.articl
## 0 0
## S.T.can S.T.compani
## 0 0
## S.T.day S.T.fashion
## 0 0
## S.T.first S.T.intern
## 0 0
## S.T.make S.T.new
## 0 0
## S.T.newyork S.T.newyorktim
## 0 0
## S.T.one S.T.presid
## 0 0
## S.T.report S.T.said
## 0 0
## S.T.share S.T.show
## 0 0
## S.T.take S.T.time
## 0 0
## S.T.week S.T.will
## 0 0
## S.T.year A.T.articl
## 0 0
## A.T.can A.T.compani
## 0 0
## A.T.day A.T.fashion
## 0 0
## A.T.first A.T.intern
## 0 0
## A.T.make A.T.new
## 0 0
## A.T.newyork A.T.newyorktim
## 0 0
## A.T.one A.T.presid
## 0 0
## A.T.report A.T.said
## 0 0
## A.T.share A.T.show
## 0 0
## A.T.take A.T.time
## 0 0
## A.T.week A.T.will
## 0 0
## A.T.year H.T.clip
## 0 0
## H.T.ebola H.T.get
## 0 0
## H.T.newyorktim H.T.read
## 0 0
## H.T.word H.nwrds.log
## 0 0
## H.nwrds.unq.log H.sum.TfIdf
## 0 0
## H.ratio.sum.TfIdf.nwrds H.nchrs.log
## 0 0
## H.nuppr.log H.ndgts.log
## 0 0
## H.npnct01.log H.npnct02.log
## 0 0
## H.npnct03.log H.npnct04.log
## 0 0
## H.npnct05.log H.npnct06.log
## 0 0
## H.npnct07.log H.npnct08.log
## 0 0
## H.npnct09.log H.npnct10.log
## 0 0
## H.npnct11.log H.npnct12.log
## 0 0
## H.npnct13.log H.npnct14.log
## 0 0
## H.npnct15.log H.npnct16.log
## 0 0
## H.npnct17.log H.npnct18.log
## 0 0
## H.npnct19.log H.npnct20.log
## 0 0
## H.npnct21.log H.npnct22.log
## 0 0
## H.npnct23.log H.npnct24.log
## 0 0
## H.npnct25.log H.npnct26.log
## 0 0
## H.npnct27.log H.npnct28.log
## 0 0
## H.npnct29.log H.npnct30.log
## 0 0
## H.nstopwrds.log H.ratio.nstopwrds.nwrds
## 0 0
## H.P.http H.P.year.colon
## 0 0
## H.P.daily.clip.report H.P.fashion.week
## 0 0
## H.P.first.draft H.P.facts.figures
## 0 0
## H.P.friday.night.music H.P.no.comment.colon
## 0 0
## H.P.on.this.day H.P.quandary
## 0 0
## H.P.readers.respond H.P.recap.colon
## 0 0
## H.P.s.notebook H.P.today.in.politic
## 0 0
## H.P.today.in.smallbusiness H.P.verbatim.colon
## 0 0
## H.P.what.we.are S.T.appear
## 0 0
## S.T.archiv S.T.diari
## 0 0
## S.T.herald S.T.obama
## 0 0
## S.T.photo S.T.senat
## 0 0
## S.T.tribun S.T.word
## 0 0
## S.nwrds.log S.nwrds.unq.log
## 0 0
## S.sum.TfIdf S.ratio.sum.TfIdf.nwrds
## 0 0
## S.nchrs.log S.nuppr.log
## 0 0
## S.ndgts.log S.npnct01.log
## 0 0
## S.npnct02.log S.npnct03.log
## 0 0
## S.npnct04.log S.npnct05.log
## 0 0
## S.npnct06.log S.npnct07.log
## 0 0
## S.npnct08.log S.npnct09.log
## 0 0
## S.npnct10.log S.npnct11.log
## 0 0
## S.npnct12.log S.npnct13.log
## 0 0
## S.npnct14.log S.npnct15.log
## 0 0
## S.npnct16.log S.npnct17.log
## 0 0
## S.npnct18.log S.npnct19.log
## 0 0
## S.npnct20.log S.npnct21.log
## 0 0
## S.npnct22.log S.npnct23.log
## 0 0
## S.npnct24.log S.npnct25.log
## 0 0
## S.npnct26.log S.npnct27.log
## 0 0
## S.npnct28.log S.npnct29.log
## 0 0
## S.npnct30.log S.nstopwrds.log
## 0 0
## S.ratio.nstopwrds.nwrds S.P.http
## 0 0
## S.P.year.colon S.P.daily.clip.report
## 0 0
## S.P.fashion.week S.P.first.draft
## 0 0
## S.P.metropolitan.diary.colon A.T.appear
## 0 0
## A.T.archiv A.T.diari
## 0 0
## A.T.herald A.T.obama
## 0 0
## A.T.photo A.T.senat
## 0 0
## A.T.tribun A.T.word
## 0 0
## A.nwrds.log A.nwrds.unq.log
## 0 0
## A.sum.TfIdf A.ratio.sum.TfIdf.nwrds
## 0 0
## A.nchrs.log A.nuppr.log
## 0 0
## A.ndgts.log A.npnct01.log
## 0 0
## A.npnct02.log A.npnct03.log
## 0 0
## A.npnct04.log A.npnct05.log
## 0 0
## A.npnct06.log A.npnct07.log
## 0 0
## A.npnct08.log A.npnct09.log
## 0 0
## A.npnct10.log A.npnct11.log
## 0 0
## A.npnct12.log A.npnct13.log
## 0 0
## A.npnct14.log A.npnct15.log
## 0 0
## A.npnct16.log A.npnct17.log
## 0 0
## A.npnct18.log A.npnct19.log
## 0 0
## A.npnct20.log A.npnct21.log
## 0 0
## A.npnct22.log A.npnct23.log
## 0 0
## A.npnct24.log A.npnct25.log
## 0 0
## A.npnct26.log A.npnct27.log
## 0 0
## A.npnct28.log A.npnct29.log
## 0 0
## A.npnct30.log A.nstopwrds.log
## 0 0
## A.ratio.nstopwrds.nwrds A.P.http
## 0 0
## A.P.year.colon A.P.daily.clip.report
## 0 0
## A.P.fashion.week A.P.first.draft
## 0 0
## A.P.metropolitan.diary.colon
## 0dsp_hdlpfx_results <- function(hdlpfx) {
print(hdlpfx)
print(glb_OOBobs_df[glb_OOBobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_newobs_df), value=TRUE)])
print(dsp_vctr <- colSums(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
setdiff(grep("[HSA]\\.", names(glb_newobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_newobs_df),
grep(".fctr", names(glb_newobs_df), fixed=TRUE, value=TRUE)))]))
print(dsp_vctr <- dsp_vctr[dsp_vctr != 0])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
union(names(dsp_vctr), myfind_chr_cols_df(glb_newobs_df))])
}
#dsp_hdlpfx_results(hdlpfx="Ask Well::")
# print("myMisc::|OpEd|blank|blank|1:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% c(6446),
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# c("WordCount", "WordCount.log", "myMultimedia",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"), ],
# c(glb_rsp_var, "myMultimedia")))
# dsp_chisq.test(Headline.contains="[Vi]deo")
# print(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline")])
# print(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola", Popular=1),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.ConditionalX.y & is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")), glb_featsimp_df))## Rank9.Bayes.glm.glm.importance
## `myCategory.fctrOpEd#Opinion#` 1.000000e+02
## WordCount.log 9.884070e+01
## `myCategory.fctrOpEd#Opinion#:WordCount.log` 7.815739e+01
## H.npnct19.log 5.218153e+01
## `myCategory.fctrMetro#N.Y. / Region#` 4.645295e+01
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` 4.408266e+01
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` 4.268518e+01
## `myCategory.fctr##:.clusterid.fctr2` 4.103112e+01
## `myCategory.fctrStyles#U.S.#` 4.000256e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 3.552842e+01
## `myCategory.fctrBusiness#Business Day#Dealbook` 3.380594e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 3.138751e+01
## `myCategory.fctrStyles#U.S.#:WordCount.log` 3.120595e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 3.018292e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 2.920092e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 2.832896e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 2.696428e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 2.549705e+01
## `myCategory.fctr##:.clusterid.fctr16` 2.419378e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 2.363321e+01
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 2.321685e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 2.268658e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 2.259505e+01
## `myCategory.fctr##:.clusterid.fctr5` 2.157671e+01
## `myCategory.fctrCulture#Arts#:WordCount.log` 2.157110e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 2.141486e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 2.054855e+01
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 2.049585e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 2.018289e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 1.954478e+01
## `myCategory.fctrCulture#Arts#` 1.942482e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 1.924374e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 1.862809e+01
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 1.856946e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 1.848269e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 1.803981e+01
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 1.668170e+01
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 1.653240e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 1.644645e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 1.641889e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 1.565868e+01
## `myCategory.fctr##:.clusterid.fctr3` 1.540990e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 1.537242e+01
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` 1.504956e+01
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` 1.492789e+01
## `myCategory.fctrScience#Health#:H.npnct19.log` 1.490172e+01
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 1.462603e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 1.460209e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 1.458054e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 1.456803e+01
## `myCategory.fctrForeign#World#Asia Pacific` 1.352566e+01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 1.339390e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 1.319672e+01
## `myCategory.fctr##:.clusterid.fctr18` 1.314696e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 1.314612e+01
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 1.314288e+01
## `myCategory.fctr##:.clusterid.fctr9` 1.314042e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 1.307802e+01
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 1.304453e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 1.295186e+01
## `myCategory.fctr##:.clusterid.fctr12` 1.276039e+01
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 1.271906e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 1.269929e+01
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 1.266601e+01
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 1.189402e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 1.187027e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 1.181831e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 1.126736e+01
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 1.066652e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 1.055727e+01
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 1.054340e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 1.052033e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 1.047047e+01
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 9.917204e+00
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 9.865082e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 9.320117e+00
## `myCategory.fctrScience#Health#:WordCount.log` 9.166309e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 8.993313e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 8.744250e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 8.643218e+00
## `myCategory.fctrTStyle##` 8.451106e+00
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 7.968954e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 7.926461e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 7.289080e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 7.111804e+00
## `myCategory.fctrBusiness#Technology#` 6.909644e+00
## `myCategory.fctrScience#Health#` 6.863985e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 6.618637e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 6.561348e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 6.457157e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 6.397139e+00
## `myCategory.fctrBusiness#Technology#:WordCount.log` 6.393663e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 6.374634e+00
## `myCategory.fctr##:.clusterid.fctr11` 6.349161e+00
## `myCategory.fctr#Opinion#Room For Debate` 6.275751e+00
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 6.259496e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 6.167661e+00
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 6.083262e+00
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 5.777986e+00
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 5.547585e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 5.485891e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 5.321709e+00
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 5.267173e+00
## `myCategory.fctr##:.clusterid.fctr14` 5.165276e+00
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 5.154312e+00
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 4.945465e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 4.901225e+00
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 4.858091e+00
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 4.847880e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 4.575708e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` 4.394841e+00
## `myCategory.fctr##:.clusterid.fctr6` 4.385923e+00
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 3.912793e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 3.726561e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 3.689364e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 3.575217e+00
## `myCategory.fctr#Multimedia#:WordCount.log` 3.307688e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 3.130503e+00
## `myCategory.fctr##:.clusterid.fctr15` 3.038014e+00
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 2.871034e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 2.857529e+00
## `myCategory.fctr##:.clusterid.fctr8` 2.844186e+00
## `myCategory.fctr##:.clusterid.fctr13` 2.624995e+00
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 2.567929e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 2.521825e+00
## `myCategory.fctr##:.clusterid.fctr19` 2.497297e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 2.412686e+00
## `myCategory.fctr##:.clusterid.fctr7` 2.265546e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 2.260944e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 2.116159e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 2.036630e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 2.034420e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 2.034308e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 1.851233e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 1.843274e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 1.825427e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 1.809042e+00
## `myCategory.fctr##:.clusterid.fctr17` 1.587524e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 1.553186e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 1.396836e+00
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 1.313310e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 9.764945e-01
## `myCategory.fctrTStyle##:WordCount.log` 7.922531e-01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 6.405127e-01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 4.058020e-01
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 3.238308e-01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 2.504980e-01
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 2.468260e-01
## `myCategory.fctr##:.clusterid.fctr10` 7.291866e-03
## `myCategory.fctrTravel#Travel#:WordCount.log` 7.039994e-03
## `myCategory.fctr##:.clusterid.fctr4` 5.667737e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 5.166561e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 5.030792e-03
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 4.917465e-03
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 4.645849e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 4.512925e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 4.119993e-03
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 3.425300e-03
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 3.361237e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 3.303799e-03
## `myCategory.fctr##:.clusterid.fctr20` 3.147075e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 3.127465e-03
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 2.799343e-03
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 1.289043e-03
## `myCategory.fctr#Multimedia#` 1.271788e-03
## `myCategory.fctrTStyle##:H.npnct19.log` 8.624905e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 6.970142e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 6.880603e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 6.800621e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 6.729538e-04
## `myCategory.fctr#U.S.#Education:WordCount.log` 6.684324e-04
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 6.670098e-04
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 6.150200e-04
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 6.114696e-04
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 4.945572e-04
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 4.462171e-04
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 4.248833e-04
## `myCategory.fctrForeign#World#:WordCount.log` 4.178432e-04
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 3.201436e-04
## `myCategory.fctrStyles##Fashion` 2.853249e-04
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 2.722640e-04
## `myCategory.fctrmyOther:WordCount.log` 2.523366e-04
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 2.499844e-04
## `myCategory.fctrStyles##Fashion:WordCount.log` 2.378297e-04
## myCategory.fctrmyOther 2.288603e-04
## `myCategory.fctrForeign#World#` 2.278260e-04
## `myCategory.fctr#U.S.#Education` 1.888960e-04
## `myCategory.fctrTravel#Travel#` 1.855409e-04
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 1.168596e-04
## `myCategory.fctrForeign#World#:H.npnct19.log` 8.573706e-05
## `myCategory.fctrmyOther:H.npnct19.log` 7.394921e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 6.095186e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 5.266618e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 5.256863e-05
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 4.863348e-05
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 4.861938e-05
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 4.852911e-05
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 4.850624e-05
## `myCategory.fctrBusiness#Crosswords/Games#` 4.843395e-05
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 4.828695e-05
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` 3.940152e-05
## `myCategory.fctr#Opinion#The Public Editor` 1.230877e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 1.062662e-05
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 8.954267e-06
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 3.828895e-09
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 2.932202e-09
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 1.750250e-09
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 1.711854e-09
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 1.273225e-09
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 5.039579e-11
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 4.652878e-11
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 4.360351e-11
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 4.129570e-11
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 4.089742e-11
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 5.416985e-12
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 3.505663e-12
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 2.827267e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 1.265256e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 1.162710e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 4.740783e-13
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 3.504635e-13
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 3.271409e-13
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 3.267741e-13
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 0.000000e+00
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` NA
## importance
## `myCategory.fctrOpEd#Opinion#` 2.791977e-06
## WordCount.log 2.890719e-06
## `myCategory.fctrOpEd#Opinion#:WordCount.log` 2.076892e-06
## H.npnct19.log 1.327560e-06
## `myCategory.fctrMetro#N.Y. / Region#` 1.164007e-06
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` 1.109756e-06
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` 1.200398e-06
## `myCategory.fctr##:.clusterid.fctr2` 1.180598e-06
## `myCategory.fctrStyles#U.S.#` 1.738666e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 6.541200e-07
## `myCategory.fctrBusiness#Business Day#Dealbook` 1.089080e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 5.014849e-07
## `myCategory.fctrStyles#U.S.#:WordCount.log` 1.543406e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 1.009766e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 6.570987e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 7.329648e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 8.293746e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 6.819898e-07
## `myCategory.fctr##:.clusterid.fctr16` 6.754565e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 6.252462e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 4.264207e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 4.900493e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 5.274079e-07
## `myCategory.fctr##:.clusterid.fctr5` 4.762285e-07
## `myCategory.fctrCulture#Arts#:WordCount.log` 1.000000e+02
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 6.135461e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 6.901887e-07
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 6.079845e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 2.996324e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 5.081137e+01
## `myCategory.fctrCulture#Arts#` 7.644081e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 5.326887e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 1.529064e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 4.657528e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 3.394719e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 4.230747e-01
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 8.347016e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 1.975440e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 6.024787e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 3.468364e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 4.211972e-07
## `myCategory.fctr##:.clusterid.fctr3` 2.917214e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 4.061727e+01
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` 5.608472e+00
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` 5.024865e+00
## `myCategory.fctrScience#Health#:H.npnct19.log` 3.954463e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 3.731183e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 2.548318e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 4.555825e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 3.311796e+01
## `myCategory.fctrForeign#World#Asia Pacific` 2.372835e+01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 3.355832e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 2.246618e-07
## `myCategory.fctr##:.clusterid.fctr18` 3.577092e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 1.677753e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 2.772469e-07
## `myCategory.fctr##:.clusterid.fctr9` 7.494891e-09
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 6.165807e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 2.861770e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 3.548080e-07
## `myCategory.fctr##:.clusterid.fctr12` 5.086707e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 4.915037e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 1.867048e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 4.271930e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 2.431814e+00
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 1.601935e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 4.851447e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 3.914690e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 5.970857e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 3.696088e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 4.063579e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 4.699404e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 1.657333e-07
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 1.498664e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 3.233295e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 4.028979e+01
## `myCategory.fctrScience#Health#:WordCount.log` 4.603708e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 8.291082e-01
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 2.573693e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 1.172059e+00
## `myCategory.fctrTStyle##` 8.311105e+01
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 1.774488e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 3.330807e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 6.385212e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 2.577686e+01
## `myCategory.fctrBusiness#Technology#` 5.789747e+01
## `myCategory.fctrScience#Health#` 3.455968e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 1.950588e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 2.406889e-07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 1.261809e-07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 9.804680e-08
## `myCategory.fctrBusiness#Technology#:WordCount.log` 4.838009e+01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 3.869019e-07
## `myCategory.fctr##:.clusterid.fctr11` 1.320384e-07
## `myCategory.fctr#Opinion#Room For Debate` 8.269772e-08
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 2.168190e-07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 2.317193e-07
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 6.076050e-08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 2.644333e+01
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 1.359223e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 2.937831e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 8.262827e-08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 2.381665e+01
## `myCategory.fctr##:.clusterid.fctr14` 2.209964e-07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 2.385979e-07
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 3.064232e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 1.505545e-07
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 3.698206e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 4.268185e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 1.312657e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` 9.176585e+00
## `myCategory.fctr##:.clusterid.fctr6` 2.373713e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 1.078283e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 2.808542e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 1.295936e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 1.289826e+01
## `myCategory.fctr#Multimedia#:WordCount.log` 1.018354e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 2.964284e-07
## `myCategory.fctr##:.clusterid.fctr15` 2.330854e-08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 6.750718e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 2.721288e+00
## `myCategory.fctr##:.clusterid.fctr8` 3.269255e-07
## `myCategory.fctr##:.clusterid.fctr13` 1.000078e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.149712e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 9.884531e-08
## `myCategory.fctr##:.clusterid.fctr19` 8.479922e-08
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 9.198535e+00
## `myCategory.fctr##:.clusterid.fctr7` 2.472192e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 5.063688e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 1.992107e-08
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 4.519862e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 8.071021e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 6.831876e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 6.780277e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 1.193881e-07
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 8.640854e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 1.529963e+01
## `myCategory.fctr##:.clusterid.fctr17` 1.118334e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 5.686369e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 4.481444e-08
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 7.621937e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 1.956538e+01
## `myCategory.fctrTStyle##:WordCount.log` 6.388548e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 2.004393e-07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 1.385439e+01
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 7.302999e-08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 8.590796e+00
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 1.272176e+01
## `myCategory.fctr##:.clusterid.fctr10` 1.646286e-10
## `myCategory.fctrTravel#Travel#:WordCount.log` 4.503984e-10
## `myCategory.fctr##:.clusterid.fctr4` 1.917750e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 4.261894e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 1.542691e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 1.752121e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 2.096444e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 1.946063e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 7.564265e+01
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 2.931514e+01
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 8.905714e-11
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 4.667580e+01
## `myCategory.fctr##:.clusterid.fctr20` 4.275494e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 2.957532e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 7.915168e+00
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 4.299126e-09
## `myCategory.fctr#Multimedia#` 1.943357e+01
## `myCategory.fctrTStyle##:H.npnct19.log` 7.296395e+00
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 5.752182e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 5.529316e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 5.457561e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 5.408422e-10
## `myCategory.fctr#U.S.#Education:WordCount.log` 3.122810e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 5.591499e-10
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 4.321850e+00
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 7.797508e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 6.183152e-01
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 3.329893e-11
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 3.595618e+00
## `myCategory.fctrForeign#World#:WordCount.log` 1.953007e-11
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 2.179798e+00
## `myCategory.fctrStyles##Fashion` 4.628230e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 2.536587e+00
## `myCategory.fctrmyOther:WordCount.log` 2.012408e+01
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 1.870565e+00
## `myCategory.fctrStyles##Fashion:WordCount.log` 8.468462e+00
## myCategory.fctrmyOther 7.538994e+00
## `myCategory.fctrForeign#World#` 1.041456e-12
## `myCategory.fctr#U.S.#Education` 1.128035e-01
## `myCategory.fctrTravel#Travel#` 3.286422e-10
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 4.546770e+00
## `myCategory.fctrForeign#World#:H.npnct19.log` 1.590428e-12
## `myCategory.fctrmyOther:H.npnct19.log` 4.473873e+00
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 4.985583e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 5.059346e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 5.041912e-09
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 8.890255e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 8.662986e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 9.072122e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 7.143572e+00
## `myCategory.fctrBusiness#Crosswords/Games#` 9.738763e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 9.281181e+00
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` 5.012660e-09
## `myCategory.fctr#Opinion#The Public Editor` 5.139739e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 5.113902e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 5.104444e-09
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 2.076501e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 1.722337e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 1.928501e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 2.002402e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 1.837710e+01
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 1.203077e+01
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 7.532345e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 4.398434e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 4.490412e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 4.858184e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 2.132648e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 2.246495e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 3.411597e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 6.711045e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 2.380649e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 0.000000e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 3.234696e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 3.071714e-12
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 3.353739e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 3.694757e-12
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` 9.963702e+00
## Final.glm.importance
## `myCategory.fctrOpEd#Opinion#` 2.791977e-06
## WordCount.log 2.890719e-06
## `myCategory.fctrOpEd#Opinion#:WordCount.log` 2.076892e-06
## H.npnct19.log 1.327560e-06
## `myCategory.fctrMetro#N.Y. / Region#` 1.164007e-06
## `myCategory.fctrMetro#N.Y. / Region#:WordCount.log` 1.109756e-06
## `myCategory.fctr#Opinion#Room For Debate:WordCount.log` 1.200398e-06
## `myCategory.fctr##:.clusterid.fctr2` 1.180598e-06
## `myCategory.fctrStyles#U.S.#` 1.738666e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr3` 6.541200e-07
## `myCategory.fctrBusiness#Business Day#Dealbook` 1.089080e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr5` 5.014849e-07
## `myCategory.fctrStyles#U.S.#:WordCount.log` 1.543406e-06
## `myCategory.fctrBusiness#Business Day#Dealbook:WordCount.log` 1.009766e-06
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr2` 6.570987e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.1` 7.329648e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr8` 8.293746e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.2` 6.819898e-07
## `myCategory.fctr##:.clusterid.fctr16` 6.754565e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.3` 6.252462e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.4` 4.264207e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.2` 4.900493e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.4` 5.274079e-07
## `myCategory.fctr##:.clusterid.fctr5` 4.762285e-07
## `myCategory.fctrCulture#Arts#:WordCount.log` 1.000000e+02
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.4` 6.135461e-07
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.1` 6.901887e-07
## `myCategory.fctrOpEd#Opinion#:H.npnct19.log` 6.079845e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.2` 2.996324e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.4` 5.081137e+01
## `myCategory.fctrCulture#Arts#` 7.644081e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr5` 5.326887e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.1` 1.529064e-07
## `myCategory.fctrOpEd#Opinion#:PubDate.day.minutes.poly.5` 4.657528e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.5` 3.394719e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr13` 4.230747e-01
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.4` 8.347016e+00
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr4` 1.975440e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.3` 6.024787e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.3` 3.468364e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr10` 4.211972e-07
## `myCategory.fctr##:.clusterid.fctr3` 2.917214e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.1` 4.061727e+01
## `myCategory.fctrForeign#World#Asia Pacific:WordCount.log` 5.608472e+00
## `myCategory.fctrBusiness#Business Day#Small Business:WordCount.log` 5.024865e+00
## `myCategory.fctrScience#Health#:H.npnct19.log` 3.954463e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.2` 3.731183e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.3` 2.548318e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.4` 4.555825e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr12` 3.311796e+01
## `myCategory.fctrForeign#World#Asia Pacific` 2.372835e+01
## `myCategory.fctrScience#Health#:.clusterid.fctr4` 3.355832e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr6` 2.246618e-07
## `myCategory.fctr##:.clusterid.fctr18` 3.577092e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.1` 1.677753e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.2` 2.772469e-07
## `myCategory.fctr##:.clusterid.fctr9` 7.494891e-09
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.4` 6.165807e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr2` 2.861770e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.2` 3.548080e-07
## `myCategory.fctr##:.clusterid.fctr12` 5.086707e-07
## `myCategory.fctrForeign#World#Asia Pacific:PubDate.day.minutes.poly.2` 4.915037e+01
## `myCategory.fctr##:PubDate.day.minutes.poly.3` 1.867048e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr2` 4.271930e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.2` 2.431814e+00
## `myCategory.fctr##:PubDate.day.minutes.poly.5` 1.601935e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:PubDate.day.minutes.poly.5` 4.851447e-07
## `myCategory.fctr##:PubDate.day.minutes.poly.2` 3.914690e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr3` 5.970857e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.4` 3.696088e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.3` 4.063579e+00
## `myCategory.fctrCulture#Arts#:.clusterid.fctr4` 4.699404e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr7` 1.657333e-07
## `myCategory.fctrBusiness#Crosswords/Games#:WordCount.log` 1.498664e+01
## `myCategory.fctrScience#Health#:PubDate.day.minutes.poly.5` 3.233295e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.3` 4.028979e+01
## `myCategory.fctrScience#Health#:WordCount.log` 4.603708e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr7` 8.291082e-01
## `myCategory.fctrBusiness#Business Day#Dealbook:H.npnct19.log` 2.573693e-07
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.1` 1.172059e+00
## `myCategory.fctrTStyle##` 8.311105e+01
## `myCategory.fctrBusiness#Technology#:H.npnct19.log` 1.774488e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.1` 3.330807e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.5` 6.385212e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.4` 2.577686e+01
## `myCategory.fctrBusiness#Technology#` 5.789747e+01
## `myCategory.fctrScience#Health#` 3.455968e-08
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.2` 1.950588e-07
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr3` 2.406889e-07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.4` 1.261809e-07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.1` 9.804680e-08
## `myCategory.fctrBusiness#Technology#:WordCount.log` 4.838009e+01
## `myCategory.fctrScience#Health#:.clusterid.fctr2` 3.869019e-07
## `myCategory.fctr##:.clusterid.fctr11` 1.320384e-07
## `myCategory.fctr#Opinion#Room For Debate` 8.269772e-08
## `myCategory.fctrStyles#U.S.#:.clusterid.fctr4` 2.168190e-07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.3` 2.317193e-07
## `myCategory.fctrStyles#U.S.#:H.npnct19.log` 6.076050e-08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.4` 2.644333e+01
## `myCategory.fctrScience#Health#:.clusterid.fctr3` 1.359223e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr2` 2.937831e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr2` 8.262827e-08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.2` 2.381665e+01
## `myCategory.fctr##:.clusterid.fctr14` 2.209964e-07
## `myCategory.fctr#Opinion#Room For Debate:PubDate.day.minutes.poly.5` 2.385979e-07
## `myCategory.fctrCulture#Arts#:H.npnct19.log` 3.064232e+01
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr4` 1.505545e-07
## `myCategory.fctrMetro#N.Y. / Region#:H.npnct19.log` 3.698206e-07
## `myCategory.fctrBusiness#Technology#:PubDate.day.minutes.poly.1` 4.268185e+00
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.3` 1.312657e-07
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr8` 9.176585e+00
## `myCategory.fctr##:.clusterid.fctr6` 2.373713e-07
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.5` 1.078283e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr16` 2.808542e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr8` 1.295936e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr5` 1.289826e+01
## `myCategory.fctr#Multimedia#:WordCount.log` 1.018354e+01
## `myCategory.fctrMetro#N.Y. / Region#:PubDate.day.minutes.poly.5` 2.964284e-07
## `myCategory.fctr##:.clusterid.fctr15` 2.330854e-08
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.3` 6.750718e-08
## `myCategory.fctrCulture#Arts#:.clusterid.fctr7` 2.721288e+00
## `myCategory.fctr##:.clusterid.fctr8` 3.269255e-07
## `myCategory.fctr##:.clusterid.fctr13` 1.000078e-07
## `myCategory.fctrOpEd#Opinion#:.clusterid.fctr7` 1.149712e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr3` 9.884531e-08
## `myCategory.fctr##:.clusterid.fctr19` 8.479922e-08
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr5` 9.198535e+00
## `myCategory.fctr##:.clusterid.fctr7` 2.472192e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr4` 5.063688e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr9` 1.992107e-08
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.5` 4.519862e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.1` 8.071021e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.3` 6.831876e+00
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.2` 6.780277e+00
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr15` 1.193881e-07
## `myCategory.fctrBusiness#Business Day#Small Business:PubDate.day.minutes.poly.4` 8.640854e+00
## `myCategory.fctrBusiness#Business Day#Small Business` 1.529963e+01
## `myCategory.fctr##:.clusterid.fctr17` 1.118334e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr12` 5.686369e-08
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr6` 4.481444e-08
## `myCategory.fctrBusiness#Business Day#Small Business:H.npnct19.log` 7.621937e+00
## `myCategory.fctrCulture#Arts#:PubDate.day.minutes.poly.5` 1.956538e+01
## `myCategory.fctrTStyle##:WordCount.log` 6.388548e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr13` 2.004393e-07
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.3` 1.385439e+01
## `myCategory.fctrStyles#U.S.#:PubDate.day.minutes.poly.1` 7.302999e-08
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.5` 8.590796e+00
## `myCategory.fctrTStyle##:PubDate.day.minutes.poly.1` 1.272176e+01
## `myCategory.fctr##:.clusterid.fctr10` 1.646286e-10
## `myCategory.fctrTravel#Travel#:WordCount.log` 4.503984e-10
## `myCategory.fctr##:.clusterid.fctr4` 1.917750e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr3` 4.261894e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr6` 1.542691e+01
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr11` 1.752121e-07
## `myCategory.fctrBusiness#Business Day#Dealbook:.clusterid.fctr14` 2.096444e-10
## `myCategory.fctrCulture#Arts#:.clusterid.fctr8` 1.946063e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr9` 7.564265e+01
## `myCategory.fctrBusiness#Technology#:.clusterid.fctr6` 2.931514e+01
## `myCategory.fctr#Opinion#Room For Debate:H.npnct19.log` 8.905714e-11
## `myCategory.fctrCulture#Arts#:.clusterid.fctr14` 4.667580e+01
## `myCategory.fctr##:.clusterid.fctr20` 4.275494e-07
## `myCategory.fctrCulture#Arts#:.clusterid.fctr11` 2.957532e+01
## `myCategory.fctrCulture#Arts#:.clusterid.fctr10` 7.915168e+00
## `myCategory.fctr#Opinion#The Public Editor:H.npnct19.log` 4.299126e-09
## `myCategory.fctr#Multimedia#` 1.943357e+01
## `myCategory.fctrTStyle##:H.npnct19.log` 7.296395e+00
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.4` 5.752182e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.5` 5.529316e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.3` 5.457561e-10
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.1` 5.408422e-10
## `myCategory.fctr#U.S.#Education:WordCount.log` 3.122810e+01
## `myCategory.fctrTravel#Travel#:PubDate.day.minutes.poly.2` 5.591499e-10
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.4` 4.321850e+00
## `myCategory.fctrForeign#World#Asia Pacific:H.npnct19.log` 7.797508e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.3` 6.183152e-01
## `myCategory.fctrTravel#Travel#:H.npnct19.log` 3.329893e-11
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.5` 3.595618e+00
## `myCategory.fctrForeign#World#:WordCount.log` 1.953007e-11
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.2` 2.179798e+00
## `myCategory.fctrStyles##Fashion` 4.628230e+00
## `myCategory.fctr#Multimedia#:PubDate.day.minutes.poly.1` 2.536587e+00
## `myCategory.fctrmyOther:WordCount.log` 2.012408e+01
## `myCategory.fctr#U.S.#Education:H.npnct19.log` 1.870565e+00
## `myCategory.fctrStyles##Fashion:WordCount.log` 8.468462e+00
## myCategory.fctrmyOther 7.538994e+00
## `myCategory.fctrForeign#World#` 1.041456e-12
## `myCategory.fctr#U.S.#Education` 1.128035e-01
## `myCategory.fctrTravel#Travel#` 3.286422e-10
## `myCategory.fctrStyles##Fashion:H.npnct19.log` 4.546770e+00
## `myCategory.fctrForeign#World#:H.npnct19.log` 1.590428e-12
## `myCategory.fctrmyOther:H.npnct19.log` 4.473873e+00
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.5` 4.985583e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.1` 5.059346e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.3` 5.041912e-09
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.3` 8.890255e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.4` 8.662986e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.2` 9.072122e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.5` 7.143572e+00
## `myCategory.fctrBusiness#Crosswords/Games#` 9.738763e+00
## `myCategory.fctrBusiness#Crosswords/Games#:PubDate.day.minutes.poly.1` 9.281181e+00
## `myCategory.fctr#Opinion#The Public Editor:WordCount.log` 5.012660e-09
## `myCategory.fctr#Opinion#The Public Editor` 5.139739e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.2` 5.113902e-09
## `myCategory.fctr#Opinion#The Public Editor:PubDate.day.minutes.poly.4` 5.104444e-09
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.2` 2.076501e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.4` 1.722337e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.1` 1.928501e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.3` 2.002402e+01
## `myCategory.fctrmyOther:PubDate.day.minutes.poly.5` 1.837710e+01
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.5` 1.203077e+01
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.4` 7.532345e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.1` 4.398434e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.3` 4.490412e+00
## `myCategory.fctr#U.S.#Education:PubDate.day.minutes.poly.2` 4.858184e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.4` 2.132648e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.3` 2.246495e+01
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.1` 3.411597e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.4` 6.711045e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.2` 2.380649e-12
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.5` 0.000000e+00
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.2` 3.234696e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.3` 3.071714e-12
## `myCategory.fctrStyles##Fashion:PubDate.day.minutes.poly.5` 3.353739e+01
## `myCategory.fctrForeign#World#:PubDate.day.minutes.poly.1` 3.694757e-12
## `myCategory.fctrBusiness#Crosswords/Games#:H.npnct19.log` 9.963702e+00print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
rm(submit_df, tmp_OOBent_df)
# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 16 predict.data.new 9 0 1638.038 1648.343 10.305
## 17 display.session.info 10 0 1648.344 NA NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 622.162 1292.730 670.569
## 14 fit.data.training 8 0 1321.857 1625.334 303.477
## 6 extract.features 3 0 42.404 262.193 219.790
## 10 fit.models 7 0 420.866 622.161 201.295
## 8 select.features 5 0 278.879 419.776 140.897
## 12 fit.models 7 2 1292.731 1315.104 22.373
## 2 inspect.data 2 0 9.497 30.891 21.394
## 7 cluster.data 4 0 262.194 278.878 16.684
## 15 fit.data.training 8 1 1625.334 1638.037 12.703
## 16 predict.data.new 9 0 1638.038 1648.343 10.305
## 4 manage.missing.data 2 2 35.435 42.372 6.938
## 13 fit.models 7 3 1315.104 1321.857 6.753
## 3 cleanse.data 2 1 30.891 35.434 4.543
## 9 partition.data.training 6 0 419.777 420.865 1.088
## 1 import.data 1 0 8.548 9.496 0.949
## 5 encode.data 2 3 42.373 42.403 0.031
## duration
## 11 670.568
## 14 303.477
## 6 219.789
## 10 201.295
## 8 140.897
## 12 22.373
## 2 21.394
## 7 16.684
## 15 12.703
## 16 10.305
## 4 6.937
## 13 6.753
## 3 4.543
## 9 1.088
## 1 0.948
## 5 0.030
## [1] "Total Elapsed Time: 1,648.343 secs"
## label step_major step_minor bgn end elapsed
## 3 fit.models_1_rpart 3 0 822.337 1292.722 470.385
## 2 fit.models_1_glm 2 0 630.818 822.337 191.519
## 1 fit.models_1_bgn 1 0 630.801 630.817 0.016
## duration
## 3 470.385
## 2 191.519
## 1 0.016
## [1] "Total Elapsed Time: 1,292.722 secs"
## R version 3.2.0 (2015-04-16)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.3 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] tcltk grid parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] gdata_2.16.1 rpart.plot_1.5.2 rpart_4.1-9
## [4] ROCR_1.0-7 gplots_2.17.0 caTools_1.17.1
## [7] dynamicTreeCut_1.62 proxy_0.4-14 tm_0.6-1
## [10] NLP_0.1-7 stringr_1.0.0 mice_2.22
## [13] Rcpp_0.11.6 plyr_1.8.2 zoo_1.7-12
## [16] sqldf_0.4-10 RSQLite_1.0.0 DBI_0.3.1
## [19] gsubfn_0.6-6 proto_0.3-10 reshape2_1.4.1
## [22] doMC_1.3.3 iterators_1.0.7 foreach_1.4.2
## [25] doBy_4.5-13 survival_2.38-1 caret_6.0-47
## [28] ggplot2_1.0.1 lattice_0.20-31
##
## loaded via a namespace (and not attached):
## [1] class_7.3-12 gtools_3.5.0 digest_0.6.8
## [4] slam_0.1-32 BradleyTerry2_1.0-6 chron_2.3-45
## [7] evaluate_0.7 e1071_1.6-4 minqa_1.2.4
## [10] SparseM_1.6 car_2.0-25 nloptr_1.0.4
## [13] Matrix_1.2-1 rmarkdown_0.6.1 labeling_0.3
## [16] splines_3.2.0 lme4_1.1-7 munsell_0.4.2
## [19] compiler_3.2.0 mgcv_1.8-6 htmltools_0.2.6
## [22] nnet_7.3-9 codetools_0.2-11 randomForest_4.6-10
## [25] brglm_0.5-9 MASS_7.3-40 bitops_1.0-6
## [28] nlme_3.1-120 gtable_0.1.2 magrittr_1.5
## [31] formatR_1.2 scales_0.2.4 KernSmooth_2.23-14
## [34] stringi_0.4-1 RColorBrewer_1.1-2 tools_3.2.0
## [37] pbkrtest_0.4-2 yaml_2.1.13 colorspace_1.2-6
## [40] knitr_1.10.5 quantreg_5.11